20.13.5 Medical Applications -- Skin Cancer, Melanoma, Skin Lesions

APP for Monitoring Skin Spots,
2019.
WWW Link. Code, Skin Spots. 1906
he app allows you to: take and organise photos of spots; compare two images of a spot side by side; email those images to someone (eg. doctor). That's all!

Xu, L., Jackowski, M., Goshtasby, A., Roseman, D., Bines, S., Yu, C., Dhawan, A., Huntley, A.,
Segmentation skin cancer images,
IVC(17), No. 1, January 1999, pp. 65-74.
Elsevier DOI Code, Segmentation. Software describe here is available from:
HTML Version. BibRef 9901

Golston, J.E., Moss, R.H., Stoecker, W.V.,
Boundary detection in skin tumor images: An overall approach and a radial search algorithm,
PR(23), No. 11, 1990, pp. 1235-1247.
Elsevier DOI 0401
BibRef

Zhang, Z., Stoecker, W.V., Moss, R.H.,
Border detection on digitized skin tumor images,
MedImg(19), No. 11, November 2000, pp. 1128-1143.
IEEE Top Reference. 0110
BibRef

Bon, F.X., Briand, E., Guichard, S., Couturaud, B., Revol, M., Servant, J.M., Dubertret, L.,
Quantitative and kinetic evolution of wound healing through image analysis,
MedImg(19), No. 7, July 2000, pp. 767-772.
IEEE Top Reference. 0110
BibRef

Hansen, G.L., Sparrow, E.M., Kokate, J.Y., Leland, K.J., Iaizzo, P.A.,
Wound status evaluation using color image processing,
MedImg(16), No. 1, February 1997, pp. 78-86.
IEEE Top Reference. 0205
BibRef

Qi, J.Y.[Jin-Yi], Huesman, R.H.,
Theoretical study of lesion detectability of MAP reconstruction using computer observers,
MedImg(20), No. 8, August 2001, pp. 815-822.
IEEE Top Reference. 0110

See also theoretical study of the contrast recovery and variance of MAP reconstructions from PET data, A. BibRef

Thirion, J.P., Calmon, G.,
Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences,
MedImg(18), No. 5, May 1999, pp. 429-441.
IEEE Top Reference. 0110
BibRef

Ganster, H., Pinz, P., Rohrer, R., Wildling, E., Binder, M., Kittler, H.,
Automated melanoma recognition,
MedImg(20), No. 3, March 2001, pp. 233-239.
IEEE Top Reference. 0110
BibRef

Grana, C., Pellacani, G., Cucchiara, R., Seidenari, S.,
A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions,
MedImg(22), No. 8, August 2003, pp. 959-964.
IEEE Abstract. 0308

See also Comments on A New Algorithm for Border Description of Polarized Light Surface Microscopic Images of Pigmented Skin Lesions. BibRef

Cheng, H.D., Cui, M.[Muyi],
Mass lesion detection with a fuzzy neural network,
PR(37), No. 6, June 2004, pp. 1189-1200.
Elsevier DOI 0405
BibRef

Zhang, Y., Goldgof, D.B., Sarkar, S., Tsap, L.V.,
A Modeling Approach for Burn Scar Assessment Using Natural Features and Elastic Property,
MedImg(23), No. 10, October 2004, pp. 1325-1329.
IEEE Abstract. 0410
BibRef

Burroni, M., Alparone, L., Argenti, F.,
Comments on 'A New Algorithm for Border Description of Polarized Light Surface Microscopic Images of Pigmented Skin Lesions',
MedImg(25), No. 12, December 2006, pp. 1655-1656.
IEEE DOI 0701

See also new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions, A. BibRef

Xu, S.C.[Shu-Chang], Ye, X.Z.[Xiu-Zi], Wu, Y.[Yin], Giron, F.[Franck], Leveque, J.L.[Jean-Luc], Querleux, B.[Bernard],
Automatic skin decomposition based on single image,
CVIU(110), No. 1, April 2008, pp. 1-6.
Elsevier DOI 0804
Skin decomposition; Melanin; Hemoglobin; Image process BibRef

Gomez, D.D.[David Delgado], Carstensen, J.M.[Jens Michael], Ersbøll, B.K.[Bjarne K.],
Collecting highly reproducible images to support dermatological medical diagnosis,
IVC(24), No. 2, 1 February 2006, pp. 186-191.
Elsevier DOI 0604
Image acquisition; Camera calibration; Diffuse illumination; Principal component analysis; Independent component analysis; Psoriasis BibRef

Gomez, D.D.[David Delgado], Butakoff, C.[Constantine], Ersboll, B.[Bjarne], Carstensen, J.M.[Jens Michael],
Automatic change detection and quantification of dermatological diseases with an application to psoriasis images,
PRL(28), No. 9, 1 July 2007, pp. 1012-1018.
Elsevier DOI 0704
Change detection; Skin lesion; Psoriasis; Image analysis BibRef

Gomez, D.D.[David Delgado], Carstensen, J.M.[Jens Michael], Ersboll, B.[Bjarne], Skov, L.[Lone], Bang, B.[Bo],
Building an Image-Based System to Automatically Score Psoriasis,
SCIA03(557-564).
Springer DOI 0310
BibRef

Maletti, G.[Gabriela], Ersbøll, B.[Bjarne],
Illumination Correction from Psoriasis Image Data,
SCIA03(549-556).
Springer DOI 0310
BibRef

Taur, J.S.[Jinshiuh S.], Lee, G.H.[Gwo-Her], Tao, C.W., Chen, C.C.[Chien-Chou], Yang, C.W.[Ching-Wen],
Segmentation of Psoriasis Vulgaris Images Using Multiresolution-Based Orthogonal Subspace Techniques,
SMC-B(36), No. 2, April 2006, pp. 390-402.
IEEE DOI 0604
BibRef

Treuillet, S.[Sylvie], Albouy, B.[Benjamin], Lucas, Y.[Yves],
Three-Dimensional Assessment of Skin Wounds Using a Standard Digital Camera,
MedImg(28), No. 5, May 2009, pp. 752-762.
IEEE DOI 0905
BibRef

Wannous, H.[Hazem], Lucas, Y.[Yves], Treuillet, S.[Sylvie], Albouy, B.[Benjamin],
A complete 3D wound assessment tool for accurate tissue classification and measurement,
ICIP08(2928-2931).
IEEE DOI 0810
BibRef
And:
Fusion of Multi-view Tissue Classification Based on Wound 3D Model,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Tang, J.[Jinshan],
A multi-direction GVF snake for the segmentation of skin cancer images,
PR(42), No. 6, June 2009, pp. 1172-1179.
Elsevier DOI 0902
Skin tumor; Boundary extraction; Gradient vector flow; Active contour; Multidirection BibRef

Yuan, X.J.[Xiao-Jing], Situ, N.[Ning], Zouridakis, G.[George],
A narrow band graph partitioning method for skin lesion segmentation,
PR(42), No. 6, June 2009, pp. 1017-1028.
Elsevier DOI 0902
Automatic skin lesion segmentation; Melanoma detection; Border detection; Active contour; Snake; Narrow band energy BibRef

Serrano, C.[Carmen], Acha, B.[Begona],
Pattern analysis of dermoscopic images based on Markov random fields,
PR(42), No. 6, June 2009, pp. 1052-1057.
Elsevier DOI 0902
Dermoscopic images; Pattern classification; Markov random field BibRef

Jiménez, A.[Amaya], Serrano, C.[Carmen], Acha, B.[Begoña],
Automatic Detection of Globules, Streaks and Pigment Network Based on Texture and Color Analysis in Dermoscopic Images,
ICIAR17(486-493).
Springer DOI 1706
BibRef

Mendoza, C.S.[Carlos S.], Serrano, C.[Carmen], Acha, B.[Begoña],
Pattern Analysis of Dermoscopic Images Based on FSCM Color Markov Random Fields,
ACIVS09(676-685).
Springer DOI 0909
BibRef

Kallel, I.F.[Imen Fourati], Bouhlel, M.S.[Mohamed Salim], Lapayre, J.C.[Jean-Christophe], Garcia, E.[Eric],
Control of dermatology image integrity using reversible watermarking,
IJIST(19), No. 1, March 2009, pp. 5-9.
DOI Link 0902
BibRef

Veredas, F., Mesa, H., Morente, L.,
Binary Tissue Classification on Wound Images With Neural Networks and Bayesian Classifiers,
MedImg(29), No. 2, February 2010, pp. 410-427.
IEEE DOI 1002
BibRef

Peserico, E.[Enoch], Silletti, A.[Alberto],
Is (N)PRI suitable for evaluating automated segmentation of cutaneous lesions?,
PRL(31), No. 16, December 2010, pp. 2464-2467.
Elsevier DOI 1011
Segmentation; Evaluation; NPRI; Melanoma BibRef

La Torre, E.[Elisabetta], Caputo, B.[Barbara], Tommasi, T.[Tatiana],
Learning methods for melanoma recognition,
IJIST(20), No. 4, December 2010, pp. 316-322.
DOI Link 1011
BibRef
Earlier: A3, A1, A2:
Melanoma Recognition Using Representative and Discriminative Kernel Classifiers,
CVAMIA06(1-12).
Springer DOI 0605

See also Discriminative cue integration for medical image annotation. BibRef

Wannous, H., Lucas, Y., Treuillet, S.,
Enhanced Assessment of the Wound-Healing Process by Accurate Multiview Tissue Classification,
MedImg(30), No. 2, February 2011, pp. 315-326.
IEEE DOI 1102
BibRef

Capdehourat, G.[Germán], Corez, A.[Andrés], Bazzano, A.[Anabella], Alonso, R.[Rodrigo], Musé, P.[Pablo],
Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions,
PRL(32), No. 16, 1 December 2011, pp. 2187-2196.
Elsevier DOI 1112
Melanoma; Dermoscopy; Pigmented skin lesion classification; Adaptive boosting; Support vector machines; Decision trees BibRef

Khan, R.[Rehanullah], Hanbury, A.[Allan], Stöttinger, J.[Julian], Bais, A.[Abdul],
Color based skin classification,
PRL(33), No. 2, 15 January 2012, pp. 157-163.
Elsevier DOI 1112
Skin detection; Skin classification; Color spaces and skin detection; Color constancy BibRef

Silva, C.S.P.[Cátia S. P.], Marcal, A.R.S.[André R. S.], Pereira, M.A.[Marta A.], Mendonça, T.[Teresa], Rozeira, J.[Jorge],
Separability Analysis of Color Classes on Dermoscopic Images,
ICIAR12(II: 268-277).
Springer DOI 1206
BibRef

Pereyra, M., Dobigeon, N., Batatia, H., Tourneret, J.,
Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model,
MedImg(31), No. 8, August 2012, pp. 1509-1520.
IEEE DOI 1208
BibRef

Zhou, Y.[Yu], Smith, M.L.[Melvyn L.], Smith, L.N.[Lyndon N.], Warr, R.[Robert],
Combinatorial photometric stereo and its application in 3D modeling of melanoma,
MVA(23), No. 5, September 2012, pp. 1029-1045.
WWW Link. 1208
BibRef

Shepherd, T., Prince, S.J.D., Alexander, D.C.,
Interactive Lesion Segmentation with Shape Priors From Offline and Online Learning,
MedImg(31), No. 9, September 2012, pp. 1698-1712.
IEEE DOI 1209
BibRef

Ma, L.[Li], Staunton, R.C.[Richard C.],
Analysis of the contour structural irregularity of skin lesions using wavelet decomposition,
PR(46), No. 1, January 2013, pp. 98-106.
Elsevier DOI 1209
Melanoma detection; Structural irregularity of contours; Wavelet decomposition; Multi-scale descriptors; Significant wavelet sub-bands BibRef

Abbas, Q.[Qaisar], Celebi, M.E., Serrano, C.[Carmen], García, I.F.[Irene Fondón], Ma, G.[Guangzhi],
Pattern classification of dermoscopy images: A perceptually uniform model,
PR(46), No. 1, January 2013, pp. 86-97.
Elsevier DOI 1209
Dermoscopy; Pattern classification; Steerable pyramid transform; Human visual system; AdaBoost; Multi-label learning BibRef

Lu, C.[Cheng], Mahmood, M.[Muhammad], Jha, N.[Naresh], Mandal, M.[Mrinal],
Detection of melanocytes in skin histopathological images using radial line scanning,
PR(46), No. 2, February 2013, pp. 509-518.
Elsevier DOI 1210
Histopathological image analysis; Object detection; Image analysis; Melanocytes BibRef

Lu, C.[Cheng], Mandal, M.[Mrinal],
Automated analysis and diagnosis of skin melanoma on whole slide histopathological images,
PR(48), No. 8, 2015, pp. 2738-2750.
Elsevier DOI 1505
Histopathological image analysis BibRef

Hani, A.F.M., Eltegani, N.M., Arshad, L., Hussein, S.H., Jamil, A., Gill, P.,
Wound model reconstruction from three-dimensional skin surface imaging using the convex hull approximation method,
IET-IPR(6), No. 5, 2012, pp. 521-533.
DOI Link 1210
BibRef

d'Alessandro, B., Dhawan, A.P.,
3-D Volume Reconstruction of Skin Lesions for Melanin and Blood Volume Estimation and Lesion Severity Analysis,
MedImg(31), No. 11, November 2012, pp. 2083-2092.
IEEE DOI 1211
BibRef

Xie, F.Y.[Feng-Ying], Bovik, A.C.[Alan C.],
Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm,
PR(46), No. 3, March 2013, pp. 1012-1019.
Elsevier DOI 1212
Dermoscopy images; Self-generating neural network; Image clustering; Automatic segmentation; Generic algorithms BibRef

Deng, Z.L.[Zi-Lin], Fan, H.D.[Hai-Di], Xie, F.Y.[Feng-Ying], Cui, Y.[Yong], Liu, J.[Jie],
Segmentation of Dermoscopy Images Based on Fully Convolutional Neural Network,
ICIP17(1732-1736)
IEEE DOI 1803
Convolutional neural networks, Feature extraction, Image analysis, Image segmentation, Lesions, Robustness, Skin, Lesion segmentation BibRef

Lu, J., Kazmierczak, E., Manton, J.H., Sinclair, R.,
Automatic Segmentation of Scaling in 2-D Psoriasis Skin Images,
MedImg(32), No. 4, April 2013, pp. 719-730.
IEEE DOI 1304
BibRef

Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.,
Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions,
MedImg(32), No. 5, May 2013, pp. 849-861.
IEEE DOI 1305
BibRef

Smaoui, N.[Nadia], Bessassi, S.[Souhir],
A Developed System for Melanoma Diagnosis,
IJCVSP(3), No. 1, 2013, pp. xx-yy.
WWW Link. 1309
BibRef

Vionnet, L., Gateau, J., Schwarz, M., Buehler, A., Ermolayev, V., Ntziachristos, V.,
24-MHz Scanner for Optoacoustic Imaging of Skin and Burn,
MedImg(33), No. 2, February 2014, pp. 535-545.
IEEE DOI 1403
biomedical optical imaging BibRef

Scharcanski, J.[Jacob], Celebi, M.E.[M. Emre], (Eds.),
Computer Vision Techniques for the Diagnosis of Skin Cancer,

Saez, A., Serrano, C., Acha, B.,
Model-Based Classification Methods of Global Patterns in Dermoscopic Images,
MedImg(33), No. 5, May 2014, pp. 1137-1147.
IEEE DOI 1405
Classification BibRef

Serna, A.[Andrés], Marcotegui, B.[Beatriz], Decencière, E.[Etienne], Baldeweck, T.[Thérèse], Pena, A.M.[Ana-Maria], Brizion, S.[Sébastien],
Segmentation of elongated objects using attribute profiles and area stability: Application to melanocyte segmentation in engineered skin,
PRL(47), No. 1, 2014, pp. 172-182.
Elsevier DOI 1408
Mathematical morphology BibRef

Lu, Y., Xie, F., Wu, Y., Jiang, Z., Meng, R.,
No Reference Uneven Illumination Assessment for Dermoscopy Images,
SPLetters(22), No. 5, May 2015, pp. 534-538.
IEEE DOI 1411
Algorithm design and analysis BibRef

Schwarz, M., Omar, M., Buehler, A., Aguirre, J., Ntziachristos, V.,
Implications of Ultrasound Frequency in Optoacoustic Mesoscopy of the Skin,
MedImg(34), No. 2, February 2015, pp. 672-677.
IEEE DOI 1502
Dermis BibRef

Korotkov, K., Quintana, J., Puig, S., Malvehy, J., Garcia, R.,
A New Total Body Scanning System for Automatic Change Detection in Multiple Pigmented Skin Lesions,
MedImg(34), No. 1, January 2015, pp. 317-338.
IEEE DOI 1502
biomedical optical imaging BibRef

Jiji, G.W.[Gnanasigamony Wiselin], Raj, P.S.J.D.[Peter Savariraj Johnson Durai],
Content-based image retrieval in dermatology using intelligent technique,
IET-IPR(9), No. 4, 2015, pp. 306-317.
DOI Link 1505
content-based retrieval BibRef

Ledoux, A.[Audrey], Richard, N.[Noël], Capelle-Laizé, A.S.[Anne-Sophie], Fernandez-Maloigne, C.[Christine],
Perceptual color hit-or-miss transform: Application to dermatological image processing,
SIViP(9), No. 5, July 2015, pp. 1081-1091.
Springer DOI 1506
BibRef

Xu, H.[Hongming], Mandal, M.[Mrinal],
Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm,
JIVP(2015), No. 1, 2015, pp. 18.
DOI Link 1507
BibRef

Lu, C.[Cheng], Ma, Z.[Zhen], Mandal, M.,
Automated segmentation of the epidermis area in skin whole slide histopathological images,
IET-IPR(9), No. 9, 2015, pp. 735-742.
DOI Link 1509
biomedical optical imaging BibRef

Kruk, M.[Michal], Swiderski, B.[Bartosz], Osowski, S.[Stanislaw], Kurek, J.[Jaroslaw], Slowinska, M.[Monika], Walecka, I.[Irena],
Melanoma recognition using extended set of descriptors and classifiers,
JIVP(2015), No. 1, 2015, pp. 43.
DOI Link 1601
BibRef

Sáez, A., Sánchez-Monedero, J., Gutiérrez, P.A., Hervás-Martínez, C.,
Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images,
MedImg(35), No. 4, April 2016, pp. 1036-1045.
IEEE DOI 1604
Color BibRef

Kasmi, R., Mokrani, K.,
Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule,
IET-IPR(10), No. 6, 2016, pp. 448-455.
DOI Link 1606
biomedical optical imaging BibRef

Bae, J.S.[Ji-Sang], Jeon, J.H.[Jae-Ho], Lee, J.Y.[Jae-Young], Kim, J.O.[Jong-Ok],
Skin Condition Estimation Using Mobile Handheld Camera,
ETRI(38), No. 4, August 2016, pp. 776-786.
DOI Link 1608
BibRef

Noroozi, N.[Navid], Zakerolhosseini, A.[Ali],
Computer assisted diagnosis of basal cell carcinoma using Z-transform features,
JVCIR(40, Part A), No. 1, 2016, pp. 128-148.
Elsevier DOI 1609
Skin cancer BibRef

Barata, C.[Catarina], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.], Rozeira, J.[Jorge],
Clinically inspired analysis of dermoscopy images using a generative model,
CVIU(151), No. 1, 2016, pp. 124-137.
Elsevier DOI 1610
BibRef
Earlier: A1, A3, A2, Only:
Improving dermoscopy image analysis using color constancy,
ICIP14(3527-3531)
IEEE DOI 1502
Melanoma. Design automation BibRef

Ghanta, S., Jordan, M.I., Kose, K., Brooks, D.H., Rajadhyaksha, M., Dy, J.G.,
A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin,
IP(26), No. 1, January 2017, pp. 172-184.
IEEE DOI 1612
Markov processes BibRef

Zortea, M.[Maciel], Flores, E.[Eliezer], Scharcanski, J.[Jacob],
A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images,
PR(64), No. 1, 2017, pp. 92-104.
Elsevier DOI 1701
Segmentation BibRef

Ferri, M.[Massimo], Tomba, I.[Ivan], Visotti, A.[Andrea], Stanganelli, I.[Ignazio],
A Feasibility Study for a Persistent Homology-Based k-Nearest Neighbor Search Algorithm in Melanoma Detection,
JMIV(57), No. 3, March 2017, pp. 324-339.
Springer DOI 1702
BibRef

Xie, F., Fan, H., Li, Y., Jiang, Z., Meng, R., Bovik, A.,
Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model,
MedImg(36), No. 3, March 2017, pp. 849-858.
IEEE DOI 1703
Cancer BibRef

Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A.,
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks,
MedImg(36), No. 4, April 2017, pp. 994-1004.
IEEE DOI 1704
Biomedical imaging BibRef

Roy, A.[Anandarup], Pal, A.[Anabik], Garain, U.[Utpal],
JCLMM: A finite mixture model for clustering of circular-linear data and its application to psoriatic plaque segmentation,
PR(66), No. 1, 2017, pp. 160-173.
Elsevier DOI 1704
BibRef
And: Erratum: PR(77), 2018, pp. 464.
Elsevier DOI 1802
Mixture model BibRef

Barata, C.[Catarina], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.],
Development of a clinically oriented system for melanoma diagnosis,
PR(69), No. 1, 2017, pp. 270-285.
Elsevier DOI 1706
Melanoma, diagnosis BibRef

Ohki, K.[Keiichi], Celebi, M.E.[M. Emre], Schaefer, G.[Gerald], Iyatomi, H.[Hitoshi],
Building of Readable Decision Trees for Automated Melanoma Discrimination,
ISVC15(II: 712-721).
Springer DOI 1601
BibRef

Schwarz, M., Soliman, D., Omar, M., Buehler, A., Ovsepian, S.V., Aguirre, J., Ntziachristos, V.,
Optoacoustic Dermoscopy of the Human Skin: Tuning Excitation Energy for Optimal Detection Bandwidth With Fast and Deep Imaging in vivo,
MedImg(36), No. 6, June 2017, pp. 1287-1296.
IEEE DOI 1706
Attenuation, Bandwidth, Detectors, Frequency response, Phantoms, Skin, Angiographic imaging, evaluation and performance, image quality assessment, optimization, optoacoustic/photo-acoustic imaging, skin, tissue modelling, vessels, visualization BibRef

Bae, J.S.[Ji-Sang], Lee, S.H.[Sang-Ho], Choi, K.S.[Kang-Sun], Kim, J.O.[Jong-Ok],
Robust skin-roughness estimation based on co-occurrence matrix,
JVCIR(46), No. 1, 2017, pp. 13-22.
Elsevier DOI 1706
Skin, roughness BibRef

Sadri, A.R.[Amir Reza], Azarianpour, S.[Sepideh], Zekri, M.[Maryam], Celebi, M.E.[Mehmet Emre], Sadri, S.[Saeid],
WN-based approach to melanoma diagnosis from dermoscopy images,
IET-IPR(11), No. 7, July 2017, pp. 475-482.
DOI Link 1707
BibRef

Yuan, Y., Chao, M., Lo, Y.C.,
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance,
MedImg(36), No. 9, September 2017, pp. 1876-1886.
IEEE DOI 1709
cancer, endoscopes, neural nets, skin, tumours, Jaccard distance, automatic skin lesion segmentation, melanoma detection challenge, Malignant tumors, BibRef

Shrivastava, S.[Shubhangi], Raj, A.N.J.[Alex Noel Joseph],
A vision-based non-contact area and volume estimation of irregular structures towards applications in wound measurement,
IJCVR(7), No. 5, 2017, pp. 489-501.
DOI Link 1709
BibRef

Pezeshk, A., Petrick, N., Chen, W., Sahiner, B.,
Seamless Lesion Insertion for Data Augmentation in CAD Training,
MedImg(36), No. 4, April 2017, pp. 1005-1015.
IEEE DOI 1704
Biomedical imaging BibRef

Adjed, F.[Faouzi], Gardezi, S.J.S.[Syed Jamal Safdar], Ababsa, F.[Fakhreddine], Faye, I.[Ibrahima], Dass, S.C.[Sarat Chandra],
Fusion of structural and textural features for melanoma recognition,
IET-CV(12), No. 2, March 2018, pp. 185-195.
DOI Link 1804
BibRef

Filko, D.[Damir], Cupec, R.[Robert], Nyarko, E.K.[Emmanuel Karlo],
Wound measurement by RGB-D camera,
MVA(29), No. 4, May 2018, pp. 633-654.
WWW Link. 1805
BibRef

Li, C.[Chao], Brost, V.[Vincent], Benezeth, Y.[Yannick], Marzani, F.[Franck], Yang, F.[Fan],
Design and evaluation of a parallel and optimized light-tissue interaction-based method for fast skin lesion assessment,
RealTimeIP(15), No. 2, August 2018, pp. 407-420.
Springer DOI 1808
BibRef

Ashour, A.S.[Amira S.], Hawas, A.R.[Ahmed Refaat], Guo, Y.H.[Yan-Hui], Wahba, M.A.[Maram A.],
A novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images,
SIViP(12), No. 7, October 2018, pp. 1311-1318.
Springer DOI 1809
BibRef

Rundo, F.[Francesco], Conoci, S.[Sabrina], Banna, G.L.[Giuseppe L.], Ortis, A.[Alessandro], Stanco, F.[Filippo], Battiato, S.[Sebastiano],
Evaluation of Levenberg-Marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up,
IET-CV(12), No. 7, October 2018, pp. 957-962.
DOI Link 1809
BibRef

Bi, L.[Lei], Kim, J.M.[Jin-Man], Ahn, E.[Euijoon], Kumar, A.[Ashnil], Feng, D.[Dagan], Fulham, M.[Michael],
Step-wise integration of deep class-specific learning for dermoscopic image segmentation,
PR(85), 2019, pp. 78-89.
Elsevier DOI 1810
Dermoscopic, Melanoma, Segmentation, Fully convolutional networks (FCN) BibRef

Ahn, E.[Euijoon], Kumar, A.[Ashnil], Fulham, M.[Michael], Feng, D.[Dagan], Kim, J.M.[Jin-Man],
Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation,
MedImg(39), No. 7, July 2020, pp. 2385-2394.
IEEE DOI 2007
Biomedical imaging, Feature extraction, Training, Skin, Diseases, Training data, Convolutional auto-encoders, unsupervised feature learning BibRef

Do, T., Hoang, T., Pomponiu, V., Zhou, Y., Chen, Z., Cheung, N., Koh, D., Tan, A., Tan, S.,
Accessible Melanoma Detection Using Smartphones and Mobile Image Analysis,
MultMed(20), No. 10, October 2018, pp. 2849-2864.
IEEE DOI 1810
Lesions, Skin, Malignant tumors, Feature extraction, Smart phones, Image color analysis, Image analysis, human-computer interface BibRef

Xia, Y., Zhang, L., Meng, L., Yan, Y., Nie, L., Li, X.,
Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework,
Cyber(48), No. 11, November 2018, pp. 3080-3091.
IEEE DOI 1810
Skin, Diseases, Visualization, Training, Hospitals, Feature extraction, Skin disease inference, transfer learning BibRef

Jaisakthi, S.M.[Seetharani Murugaiyan], Mirunalini, P.[Palaniappan], Aravindan, C.[Chandrabose],
Automated skin lesion segmentation of dermoscopic images using GrabCut and k-means algorithms,
IET-CV(12), No. 8, December 2018, pp. 1088-1095.
DOI Link 1812
BibRef

Sultana, N.N.[Nazneen N.], Mandal, B.[Bappaditya], Puhan, N.B.,
Deep residual network with regularised fisher framework for detection of melanoma,
IET-CV(12), No. 8, December 2018, pp. 1096-1104.
DOI Link 1812
BibRef

Creswell, A.[Antonia], Pouplin, A.[Alison], Bharath, A.A.[Anil A.],
Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data,
IET-CV(12), No. 8, December 2018, pp. 1105-1111.
DOI Link 1812
BibRef

Guo, K.[Kehua], He, Y.[Yan], Kui, X.Y.[Xiao-Yan], Sehdev, P.[Paramjit], Chi, T.[Tao], Zhang, R.[Ruifang], Li, J.[Jialun],
LLTO: Towards efficient lesion localization based on template occlusion strategy in intelligent diagnosis,
PRL(116), 2018, pp. 225-232.
Elsevier DOI 1812
BibRef

Huang, L.[Lin], Zhao, Y.G.[Yi-Gong], Yang, T.J.[Tie-Jun],
Skin lesion segmentation using object scale-oriented fully convolutional neural networks,
SIViP(13), No. 3, April 2019, pp. 431-438.
WWW Link. 1904
BibRef

Ren, S.[Sheng], Jain, D.K.[Deepak Kumar], Guo, K.[Kehua], Xu, T.[Tao], Chi, T.[Tao],
Towards efficient medical lesion image super-resolution based on deep residual networks,
SP:IC(75), 2019, pp. 1-10.
Elsevier DOI 1906
Super-resolution, Deep residual networks, Medical diagnosis BibRef

Yang, T.J.[Tie-Jun], Chen, Y.W.[Yao-Wen], Lu, J.[Jiewei], Fan, Z.[Zhun],
Sampling with level set for pigmented skin lesion segmentation,
SIViP(13), No. 4, June 2019, pp. 813-821.
Springer DOI 1906
BibRef

Nguyen, K.L., Delachartre, P., Berthier, M.,
Multi-Grid Phase Field Skin Tumor Segmentation in 3D Ultrasound Images,
IP(28), No. 8, August 2019, pp. 3678-3687.
IEEE DOI 1907
biomedical ultrasonics, image segmentation, medical image processing, nonparametric statistics, skin, tumours, exact solutions BibRef

Zhang, J., Xie, Y., Xia, Y., Shen, C.,
Attention Residual Learning for Skin Lesion Classification,
MedImg(38), No. 9, September 2019, pp. 2092-2103.
IEEE DOI 1909
Lesions, Skin, Melanoma, Learning systems, Task analysis, Visualization, Computational modeling, Attention learning, dermoscopy images BibRef

Mirbeik-Sabzevari, A., Oppelaar, E., Ashinoff, R., Tavassolian, N.,
High-Contrast, Low-Cost, 3-D Visualization of Skin Cancer Using Ultra-High-Resolution Millimeter-Wave Imaging,
MedImg(38), No. 9, September 2019, pp. 2188-2197.
IEEE DOI 1909
Imaging, Skin cancer, Surgery, Tumors, Millimeter wave technology, Skin, Millimeter-wave imaging, skin cancer imaging, malignant skin tissue BibRef

Sevik, U.[Ugur], Karakullukçu, E.[Erdinç], Berber, T.[Tolga], Akbas, Y.[Yesim], Türkyilmaz, S.[Serdar],
Automatic classification of skin burn colour images using texture-based feature extraction,
IET-IPR(13), No. 11, 19 September 2019, pp. 2018-2028.
DOI Link 1909
BibRef

Sarkar, R.[Rahul], Chatterjee, C.C.[Chandra Churh], Hazra, A.[Animesh],
Diagnosis of melanoma from dermoscopic images using a deep depthwise separable residual convolutional network,
IET-IPR(13), No. 12, October 2019, pp. 2130-2142.
DOI Link 1911
BibRef

Khan, M.A.[Muhammad Attique], Sharif, M.[Muhammad], Akram, T.[Tallha], Bukhari, S.A.C.[Syed Ahmad Chan], Nayak, R.S.[Ramesh Sunder],
Developed Newton-Raphson based deep features selection framework for skin lesion recognition,
PRL(129), 2020, pp. 293-303.
Elsevier DOI 2001
Skin cancer, Contrast stretching, Lesion localization, Deep features, Best features BibRef

Wang, X., Jiang, X., Ding, H., Liu, J.,
Bi-Directional Dermoscopic Feature Learning and Multi-Scale Consistent Decision Fusion for Skin Lesion Segmentation,
IP(29), 2020, pp. 3039-3051.
IEEE DOI 2002
Lesions, Skin, Image segmentation, Melanoma, Bidirectional control, Reliability, Feature extraction, Skin lesion segmentation, multi-scale consistent decision fusion BibRef

Nitkunanantharajah, S., Zahnd, G., Olivo, M., Navab, N., Mohajerani, P., Ntziachristos, V.,
Skin Surface Detection in 3D Optoacoustic Mesoscopy Based on Dynamic Programming,
MedImg(39), No. 2, February 2020, pp. 458-467.
IEEE DOI 2002
Skin, Imaging, Image segmentation, surface segmentation BibRef

Serte, S.[Sertan], Demirel, H.[Hasan],
Wavelet-based deep learning for skin lesion classification,
IET-IPR(14), No. 4, 27 March 2020, pp. 720-726.
DOI Link 2003
BibRef

Amin, J.[Javeria], Sharif, A.[Abida], Gul, N.[Nadia], Anjum, M.A.[Muhammad Almas], Nisar, M.W.[Muhammad Wasif], Azam, F.[Faisal], Bukhari, S.A.C.[Syed Ahmad Chan],
Integrated design of deep features fusion for localization and classification of skin cancer,
PRL(131), 2020, pp. 63-70.
Elsevier DOI 2004
Cells, Principle component analysis (PCA), Melanoma, Alexnet, VGG-16 BibRef

Jayapriya, K.[Kalyanakumar], Jacob, I.J.[Israel Jeena],
Hybrid fully convolutional networks-based skin lesion segmentation and melanoma detection using deep feature,
IJIST(30), No. 2, 2020, pp. 348-357.
DOI Link 2005
deep residual network (DRN), hybrid fully convolutional networks, melanoma detection BibRef

Zhou, W., Chen, Z., Zhou, Q., Xing, D.,
Optical Biopsy of Melanoma and Basal Cell Carcinoma Progression by Noncontact Photoacoustic and Optical Coherence Tomography: In Vivo Multi-Parametric Characterizing Tumor Microenvironment,
MedImg(39), No. 6, June 2020, pp. 1967-1974.
IEEE DOI 2006
Photoacoustic imaging, OCT, skin, multi-modality fusion, integration of multiscale information BibRef

Phan, T.B.[Tan-Binh], Trinh, D.H.[Dinh-Hoan], Wolf, D.[Didier], Daul, C.[Christian],
Optical flow-based structure-from-motion for the reconstruction of epithelial surfaces,
PR(105), 2020, pp. 107391.
Elsevier DOI 2006
3D Image mosaicing, Structure-from-Motion (SfM), Dense optical flow, Endoscopy, Dermatology BibRef

Xie, Y., Zhang, J., Xia, Y., Shen, C.,
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification,
MedImg(39), No. 7, July 2020, pp. 2482-2493.
IEEE DOI 2007
Lesions, Image segmentation, Skin, Task analysis, Feature extraction, Decoding, Computational modeling, Skin lesion segmentation, dermoscopy BibRef

de A. Rodrigues, D.[Douglas], Ivo, R.F.[Roberto F.], Satapathy, S.C.[Suresh Chandra], Wang, S.[Shuihua], Hemanth, J.[Jude], Filho, P.P.R.[Pedro P. Rebouças],
A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system,
PRL(136), 2020, pp. 8-15.
Elsevier DOI 2008
Skin lesion, Transfer learning, CNN, Internet of things BibRef

Xiao, F.[Feng], Wu, Q.[Qiuxia],
Visual saliency based global-local feature representation for skin cancer classification,
IET-IPR(14), No. 10, August 2020, pp. 2140-2148.
DOI Link 2008
BibRef

Nikesh, P., Raju, G.,
Directional Vector-Based Skin Lesion Segmentation: A Novel Approach to Skin Segmentation,
IJIG(20), No. 3, July 2020, pp. 2050021.
DOI Link 2008
BibRef

Bi, L.[Lei], Feng, D.D.[David Dagan], Fulham, M.[Michael], Kim, J.M.[Jin-Man],
Multi-Label classification of multi-modality skin lesion via hyper-connected convolutional neural network,
PR(107), 2020, pp. 107502.
Elsevier DOI 2008
Classification, Melanoma, Convolutional neural networks (cnns) BibRef

Jiji, G.W.[G. Wiselin], Rajesh, A., Raj, P.J.D.[P. Johnson Durai],
Decision Support Techniques for Dermatology Using Case-Based Reasoning,
IJIG(20), No. 3, July 2020, pp. 2050024.
DOI Link 2008
BibRef

Chakkaravarthy, A.P.[A. Prabhu], Chandrasekar, A.,
Anatomical region segmentation method from dermoscopic images of pigmented skin lesions,
IJIST(30), No. 3, 2020, pp. 636-652.
DOI Link 2008
canny edge detection, color segmentation, Gaussian filter, image gradient, morphological dilation, salt and pepper noise, watershed segmentation BibRef

Divya, D., Ganeshbabu, T.R.,
Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection,
IJIST(30), No. 3, 2020, pp. 731-752.
DOI Link 2008
dermoscopic image, melanoma skin cancer, modified deer hunting algorithm, optimized region growing, recurrent neural network BibRef

Kalra, M.[Meeta], Bouguila, N.[Nizar], Fan, W.[Wentao],
Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis,
IJIST(30), No. 3, 2020, pp. 794-814.
DOI Link 2008
biomedical images, brain tumor detection, CAD of malaria, colon cancer, diabetic retinopathy, feature extraction, skin melanoma BibRef

Genina, E.A., Surkov, Y.I., Serebryakova, I.A., Bashkatov, A.N., Tuchin, V.V., Zharov, V.P.,
Rapid Ultrasound Optical Clearing of Human Light and Dark Skin,
MedImg(39), No. 10, October 2020, pp. 3198-3206.
IEEE DOI 2010
Skin, Optical imaging, Biomedical optical imaging, Optical refraction, Optical variables control, sonophoresis BibRef

Vasconcelos, C.N.[Cristina Nader], Vasconcelos, B.N.[Bárbara Nader],
Experiments using deep learning for dermoscopy image analysis,
PRL(139), 2020, pp. 95-103.
Elsevier DOI 2011
Skin lesion classification, Dermoscopy image analysis, Lesion dermoscopic feature extraction and classification, Deep learning BibRef

Barata, C.[Catarina], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.],
Explainable skin lesion diagnosis using taxonomies,
PR(110), 2021, pp. 107413.
Elsevier DOI 2011
Hierarchical deep learning, Explainability, Channel attention, Spatial attention, Safety-critical CADS, Skin cancer BibRef

Wang, Q., Sun, L., Wang, Y., Zhou, M., Hu, M., Chen, J., Wen, Y., Li, Q.,
Identification of Melanoma From Hyperspectral Pathology Image Using 3D Convolutional Networks,
MedImg(40), No. 1, January 2021, pp. 218-227.
IEEE DOI 2012
Microscopy, segmentation, skin, quantification and estimation, optical imaging BibRef

Wu, H., Pan, J., Li, Z., Wen, Z., Qin, J.,
Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module,
MedImg(40), No. 1, January 2021, pp. 357-370.
IEEE DOI 2012
Lesions, Skin, Image segmentation, Feature extraction, Melanoma, Task analysis, Shape, Skin lesion segmentation, deep learning BibRef

Xie, Y., Zhang, J., Lu, H., Shen, C., Xia, Y.,
SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors,
MedImg(40), No. 1, January 2021, pp. 286-296.
IEEE DOI 2012
Image segmentation, Lesions, Medical diagnostic imaging, Skin, Glands, Retina, Medical image segmentation, correction learning BibRef

Cai, J., Harrison, A.P., Zheng, Y., Yan, K., Huo, Y., Xiao, J., Yang, L., Lu, L.,
Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale,
MedImg(40), No. 1, January 2021, pp. 59-70.
IEEE DOI 2012
Lesions, Proposals, Computed tomography, Training, Detectors, Biomedical imaging, pseudo 3D IoU BibRef

Khan, M.A.[Muhammad Attique], Akram, T.[Tallha], Zhang, Y.D.[Yu-Dong], Sharif, M.[Muhammad],
Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework,
PRL(143), 2021, pp. 58-66.
Elsevier DOI 2102
Skin cancer, Mask RCNN, Transfer learning, Optimal features, ELM BibRef

Jiji, G.W.[G. Wiselin], Rajesh, A., Raj, P.J.D.[P. Johnson Durai],
CBI + R: A Fusion Approach to Assist Dermatological Diagnoses,
IJIG(21), No. 1 2021, pp. 2150005.
DOI Link 2102
BibRef

Grove, R., Green, R.,
Melanoma and Nevi Classification using Convolution Neural Networks,
IVCNZ20(1-6)
IEEE DOI 2012
Training, Convolution, Neural networks, Focusing, Melanoma, Lesions, Testing, melanoma, ResNet50, identification, classification BibRef

Sabri, M.A., Filali, Y., El Khoukhi, H., Aarab, A.,
Skin Cancer Diagnosis Using an Improved Ensemble Machine Learning model,
ISCV20(1-5)
IEEE DOI 2011
cancer, feature extraction, image classification, learning (artificial intelligence), medical image processing, ensemble learning. BibRef

Bakkouri, I.[Ibtissam], Afdel, K.[Karim],
Dermonet: A Computer-aided Diagnosis System for Dermoscopic Disease Recognition,
ICISP20(170-177).
Springer DOI 2009
BibRef

Kumar, A., Hamarneh, G., Drew, M.S.,
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images,
ISIC20(3132-3141)
IEEE DOI 2008
Skin, Lesions, Image color analysis, Image segmentation, Gray-scale, Lighting, Semantics BibRef

Bagchi, S., Banerjee, A., Bathula, D.R.,
Learning A Meta-Ensemble Technique For Skin Lesion Classification And Novel Class Detection,
ISIC20(3221-3228)
IEEE DOI 2008
Stacking, Training, Skin, Task analysis, Lesions, Image color analysis, Melanoma BibRef

Coppola, D., Lee, H.K., Guan, C.,
Interpreting mechanisms of prediction for skin cancer diagnosis using multi-task learning,
ISIC20(3162-3171)
IEEE DOI 2008
Task analysis, Lesions, Logic gates, Skin, Machine learning, Melanoma BibRef

Singh, N., Lee, K., Coz, D., Angermueller, C., Huang, S., Loh, A., Liu, Y.,
Agreement Between Saliency Maps and Human-Labeled Regions of Interest: Applications to Skin Disease Classification,
ISIC20(3172-3181)
IEEE DOI 2008
Skin, Diseases, Google, Analytical models, Dermatology, Data models, Image segmentation BibRef

Ribeiro, V., Avila, S., Valle, E.,
Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation,
ISIC20(3182-3191)
IEEE DOI 2008
Image segmentation, Lesions, Training, Machine learning, Skin, Task analysis, Data models BibRef

Bissoto, A., Valle, E., Avila, S.,
Debiasing Skin Lesion Datasets and Models? Not So Fast,
ISIC20(3192-3201)
IEEE DOI 2008
Lesions, Correlation, Skin, Task analysis, Training, Feature extraction, Data models BibRef

Barata, C., Santiago, C.,
How Important Is Each Dermoscopy Image?,
ISIC20(3202-3210)
IEEE DOI 2008
Training, Lesions, Skin, Task analysis, Neural networks, Computer architecture, Image analysis BibRef

Combalia, M., Hueto, F., Puig, S., Malvehy, J., Vilaplana, V.,
Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification,
ISIC20(3211-3220)
IEEE DOI 2008
Uncertainty, Lesions, Skin, Neural networks, Monte Carlo methods, Training, Task analysis BibRef

Pacheco, A.G.C., Sastry, C.S., Trappenberg, T., Oore, S., Krohling, R.A.,
On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers,
ISIC20(3152-3161)
IEEE DOI 2008
Training, Melanoma, Neural networks, Detection algorithms, Dogs, Skin BibRef

Mahajan, K., Sharma, M., Vig, L.,
Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning,
ISIC20(3142-3151)
IEEE DOI 2008
Skin, Diseases, Lesions, Task analysis, Training, Biomedical imaging, Adaptation models BibRef

Andrade, C.[Catarina], Teixeira, L.F.[Luís F.], Vasconcelos, M.J.M.[Maria João M.], Rosado, L.[Luís],
Deep Learning Models for Segmentation of Mobile-acquired Dermatological Images,
ICIAR20(II:228-237 Open Access).
Springer DOI 2007
BibRef

Bekmirzaev, S., Oh, S., Yo, S.,
RethNet: Object-by-Object Learning for Detecting Facial Skin Problems,
VRMI19(425-433)
IEEE DOI 2004
computer vision, face recognition, feature extraction, image segmentation, learning (artificial intelligence), fine grained object categorization BibRef

Wu, X., Wen, N., Liang, J., Lai, Y., She, D., Cheng, M., Yang, J.,
Joint Acne Image Grading and Counting via Label Distribution Learning,
ICCV19(10641-10650)
IEEE DOI 2004
Code, Dermatology.
WWW Link. diseases, learning (artificial intelligence), medical image processing, skin, joint acne image grading, Training BibRef

Adegun, A.[Adekanmi], Viriri, S.[Serestina],
Deep Convolutional Network-based Framework for Melanoma Lesion Detection and Segmentation,
ACIVS20(51-62).
Springer DOI 2003
BibRef

Dogvanich, A., Mamaev, N., Krylov, A., Makhneva, N.,
Dermatological Image Denoising Using Adaptive Henlm Method,
PTVSBB19(47-52).
DOI Link 1912
BibRef

Gavrilov, D.A., Shchelkunov, N.N., Melerzanov, A.V.,
Deep Learning Based Skin Lesions Diagnosis,
PTVSBB19(81-85).
DOI Link 1912
BibRef

Peng, J., Gao, R., Nguyen, L., Liang, Y., Thng, S., Lin, Z.,
Classification of Non-Tumorous Facial Pigmentation Disorders Using Improved Smote and Transfer Learning,
ICIP19(220-224)
IEEE DOI 1910
improved SMOTE, facial pigmentation disorders, biomedical images analysis and classification, transfer learning BibRef

Tu, W., Liu, X., Hu, W., Pan, Z., Xu, X., Li, B.,
Segmentation of Lesion in Dermoscopy Images Using Dense-Residual Network with Adversarial Learning,
ICIP19(1430-1434)
IEEE DOI 1910
dermoscopic image, skin lesion, convolutional neural networks, adversarial learning, Dense-Residual block BibRef

Wang, X., Ding, H., Jiang, X.,
Dermoscopic Image Segmentation Through the Enhanced High-Level Parsing and Class Weighted Loss,
ICIP19(245-249)
IEEE DOI 1910
Skin lesion segmentation, fully convolutional neural network, enhanced high-level parsing, class weighed loss BibRef

Ferreira, B.[Bárbara], Barata, C.[Catarina], Marques, J.S.[Jorge S.],
What Is the Role of Annotations in the Detection of Dermoscopic Structures?,
IbPRIA19(II:3-11).
Springer DOI 1910
BibRef

Franco-Ceballos, R.[Ricardo], Torres-Madronero, M.C.[Maria C.], Galeano-Zea, J.[July], Murillo, J.[Javier], Zarzycki, A.[Artur], Garzon, J.[Johnson], Robledo, S.M.[Sara M.],
Spectral Band Subset Selection for Discrimination of Healthy Skin and Cutaneous Leishmanial Ulcers,
IbPRIA19(I:398-408).
Springer DOI 1910
BibRef

Carcagnì, P.[Pierluigi], Leo, M.[Marco], Cuna, A.[Andrea], Mazzeo, P.L.[Pier Luigi], Spagnolo, P.[Paolo], Celeste, G.[Giuseppe], Distante, C.[Cosimo],
Classification of Skin Lesions by Combining Multilevel Learnings in a DenseNet Architecture,
CIAP19(I:335-344).
Springer DOI 1909
BibRef

Bonechi, S.[Simone], Bianchini, M.[Monica], Bongini, P.[Pietro], Ciano, G.[Giorgio], Giacomini, G.[Giorgia], Rosai, R.[Riccardo], Tognetti, L.[Linda], Rossi, A.[Alberto], Andreini, P.[Paolo],
Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning,
NTIAP19(211-219).
Springer DOI 1909
BibRef

Piantadosi, G.[Gabriele], Bovenzi, G.[Giampaolo], Argenziano, G.[Giuseppe], Moscarella, E.[Elvira], Parmeggiani, D.[Domenico], Docimo, L.[Ludovico], Sansone, C.[Carlo],
Skin Lesions Classification: A Radiomics Approach with Deep CNN,
NTIAP19(252-259).
Springer DOI 1909
BibRef

Adegun, A.[Adekanmi], Viriri, S.[Serestina],
Deep Learning Model for Skin Lesion Segmentation: Fully Convolutional Network,
ICIAR19(II:232-242).
Springer DOI 1909
BibRef

Neher, H.[Helmut], Arlette, J.[John], Wong, A.[Alexander],
Discovery Radiomics for Detection of Severely Atypical Melanocytic Lesions (SAML) from Skin Imaging via Deep Residual Group Convolutional Radiomic Sequencer,
ICIAR19(II:307-315).
Springer DOI 1909
BibRef

Canalini, L.[Laura], Pollastri, F.[Federico], Bolelli, F.[Federico], Cancilla, M.[Michele], Allegretti, S.[Stefano], Grana, C.[Costantino],
Skin Lesion Segmentation Ensemble with Diverse Training Strategies,
CAIP19(I:89-101).
Springer DOI 1909
BibRef

Czovny, R.K., Bellon, O.R.P., Silva, L., Costa, H.S.G.,
Minutia Matching using 3D Pore Clouds,
ICPR18(3138-3143)
IEEE DOI 1812
Dermis, Epidermis, Databases, Biomedical imaging, Measurement uncertainty BibRef

Yang, J., Sun, X., Liang, J., Rosin, P.L.,
Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria,
CVPR18(1258-1266)
IEEE DOI 1812
Skin, Lesions, Diseases, Image color analysis, Medical diagnostic imaging, Shape BibRef

Li, H., He, X., Yu, Z., Zhou, F., Cheng, J., Huang, L., Wang, T., Lei, B.,
Skin Lesion Segmentation via Dense Connected Deconvolutional Network,
ICPR18(671-675)
IEEE DOI 1812
Lesions, Skin, Decoding, Training, Convolution, Imaging, Image restoration, Skin lesion segmentation, Dermoscopy image, Chained residual pooling BibRef

Luo, W., Yang, M.,
Fast Skin Lesion Segmentation via Fully Convolutional Network with Residual Architecture and CRF,
ICPR18(1438-1443)
IEEE DOI 1812
Lesions, Image segmentation, Convolution, Skin, Kernel, Pipelines, Training, Melanoma, Fully Convolutional Network, Conditional Random Field BibRef

Salih, O.[Omran], Viriri, S.[Serestina],
Skin Cancer Segmentation Using a Unified Markov Random Field,
ISVC18(25-33).
Springer DOI 1811
BibRef

Elmogy, M.[Mohammed], García-Zapirain, B.[Begoña], Elmaghraby, A.S.[Adel S.], Ei-Baz, A.[Ayman],
An Automated Classification Framework for Pressure Ulcer Tissues Based on 3D Convolutional Neural Network,
ICPR18(2356-2361)
IEEE DOI 1812
Image segmentation, Image color analysis, Solid modeling, Skin, Kernel, Feature extraction BibRef

Elmogy, M.[Mohammed], García-Zapirain, B.[Begoña], Burns, C.[Connor], Elmaghraby, A.S.[Adel S.], Ei-Baz, A.[Ayman],
Tissues Classification for Pressure Ulcer Images Based on 3D Convolutional Neural Network,
ICIP18(3139-3143)
IEEE DOI 1809
Image segmentation, Kernel, Image color analysis, Biological system modeling, Solid modeling, Linear Combinations of Discrete Gaussians (LCDG) BibRef

Filali, Y., Ennouni, A., Sabri, M.A., Aarab, A.,
A study of lesion skin segmentation, features selection and classification approaches,
ISCV18(1-7)
IEEE DOI 1807
cancer, feature extraction, feature selection, image classification, image colour analysis, image segmentation, machine-learning BibRef

Zeng, G.D.[Guo-Dong], Zheng, G.[Guoyan],
Multi-scale Fully Convolutional DenseNets for Automated Skin Lesion Segmentation in Dermoscopy Images,
ICIAR18(513-521).
Springer DOI 1807
BibRef

Coronado, R.[Ricardo], Ocsa, A.[Alexander], Quispe, O.[Oscar],
Non-dermatoscopic Image Analysis for the Recognition of Malignant Skin Diseases with Convolutional Neural Network and Autoencoders,
CIARP17(160-167).
Springer DOI 1802
BibRef

Mahdiraji, S.A., Baleghi, Y., Sakhaei, S.M.,
Skin lesion images classification using new color pigmented boundary descriptors,
IPRIA17(102-107)
IEEE DOI 1712
cameras, cancer, feature extraction, image classification, image colour analysis, image texture, medical image processing, Skin lesion BibRef

Rundo, F.[Francesco], Conoci, S.[Sabrina], Banna, G.L.[Giuseppe L.], Stanco, F.[Filippo], Battiato, S.[Sebastiano],
Bio-Inspired Feed-Forward System for Skin Lesion Analysis, Screening and Follow-Up,
CIAP17(II:399-409).
Springer DOI 1711
BibRef

Balducci, F.[Fabrizio], Grana, C.[Costantino],
Pixel Classification Methods to Detect Skin Lesions on Dermoscopic Medical Images,
CIAP17(II:444-455).
Springer DOI 1711
BibRef

Al-abayechi, A.A.A.[Alaa Ahmed Abbas], Jalab, H.A.[Hamid A.], Ibrahim, R.W.[Rabha W.], Hasan, A.M.[Ali M.],
Image Enhancement Based on Fractional Poisson for Segmentation of Skin Lesions Using the Watershed Transform,
IVIC17(249-259).
Springer DOI 1711
BibRef

Bozkurt, A., Gale, T., Kose, K., Alessi-Fox, C., Brooks, D.H., Rajadhyaksha, M., Dy, J.G.,
Delineation of Skin Strata in Reflectance Confocal Microscopy Images with Recurrent Convolutional Networks,
Microscopy17(777-785)
IEEE DOI 1709
Dermis, Epidermis, Feature extraction, Imaging, Training BibRef

Gu, Y., Zhou, J., Qian, B.,
Melanoma Detection Based on Mahalanobis Distance Learning and Constrained Graph Regularized Nonnegative Matrix Factorization,
WACV17(797-805)
IEEE DOI 1609
Feature extraction, Linear programming, Malignant tumors, Manifolds, Matrix decomposition, Skin, Skin, cancer BibRef

Barata, C.[Catarina], Figueiredo, M.A.T.[Mário A. T.], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.],
Local Features Applied to Dermoscopy Images: Bag-of-Features versus Sparse Coding,
IbPRIA17(528-536).
Springer DOI 1706
BibRef

Alarifi, J.S.[Jhan S.], Goyal, M.[Manu], Davison, A.K.[Adrian K.], Dancey, D.[Darren], Khan, R.[Rabia], Yap, M.H.[Moi Hoon],
Facial Skin Classification Using Convolutional Neural Networks,
ICIAR17(479-485).
Springer DOI 1706
BibRef

Cho, D.S.[Daniel S.], Khalvati, F.[Farzad], Clausi, D.A.[David A.], Wong, A.[Alexander],
A Machine Learning-Driven Approach to Computational Physiological Modeling of Skin Cancer,
ICIAR17(79-86).
Springer DOI 1706
BibRef

Hajdu, A., Harangi, B., Besenczi, R., Lázár, I., Emri, G., Hajdu, L., Tijdeman, R.,
Measuring regularity of network patterns by grid approximations using the LLL algorithm,
ICPR16(1524-1529)
IEEE DOI 1705
Approximation algorithms, Lesions, Measurement uncertainty, Noise level, Pattern recognition, Pigments, Skin BibRef

Kaur, P., Dana, K.J., Cula, G.O., Mack, M.C.,
Hybrid deep learning for Reflectance Confocal Microscopy skin images,
ICPR16(1466-1471)
IEEE DOI 1705
Epidermis, Histograms, Image recognition, Libraries, Machine learning, Neural, networks BibRef

Pal, A., Chaturvedi, A., Garain, U., Chandra, A., Chatterjee, R.,
Severity grading of psoriatic plaques using deep CNN based multi-task learning,
ICPR16(1478-1483)
IEEE DOI 1705
Computer architecture, Convolution, Diseases, Drugs, Estimation, Kernel, Skin BibRef

Liao, H.[Haofu], Li, Y.[Yuncheng], Luo, J.B.[Jie-Bo],
Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks,
ICPR16(355-360)
IEEE DOI 1705
Dermatology, Diseases, Lesions, Malignant tumors, Skin, Training, Visualization, convolutional neural networks, skin disease classification, skin, lesion, characterization BibRef

Jafari, M.H., Karimi, N., Nasr-Esfahani, E., Samavi, S., Soroushmehr, S.M.R., Ward, K., Najarian, K.,
Skin lesion segmentation in clinical images using deep learning,
ICPR16(337-342)
IEEE DOI 1705
Feature extraction, Image segmentation, Lesions, Lighting, Machine learning, Malignant tumors, Skin, Melanoma, convolutional neural network, deep learning, medical image segmentation, skin, cancer BibRef

Faraz, K.[Khuram], Blondel, W.[Walter], Amouroux, M.[Marine], Daul, C.[Christian],
Towards skin image mosaicing,
IPTA16(1-6)
IEEE DOI 1703
Tele-dermatology. feature extraction BibRef

Majtner, T., Yildirim-Yayilgan, S., Hardeberg, J.Y.,
Combining deep learning and hand-crafted features for skin lesion classification,
IPTA16(1-6)
IEEE DOI 1703
biomedical optical imaging BibRef

Haji Rassouliha, A., Kmiecik, B., Taberner, A.J., Nash, M.P., Nielsen, P.M.F.,
A Low-cost, hand-held stereoscopic device for measuring dynamic deformations of skin in vivo,
ICVNZ15(1-6)
IEEE DOI 1701
deformation BibRef

Yao, T.T.[Ting-Ting], Wang, Z.Y.[Zhi-Yong], Xie, Z.[Zhao], Gao, J.[Jun], Feng, D.D.[David Dagan],
A Multiview Joint Sparse Representation with Discriminative Dictionary for Melanoma Detection,
DICTA16(1-6)
IEEE DOI 1701
Dictionaries BibRef

Kawahara, J.[Jeremy], Hamarneh, G.[Ghassan],
Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers,
MLMI16(164-171).
Springer DOI 1611
BibRef

Bozorgtabar, B.[Behzad], Abedini, M.[Mani], Garnavi, R.[Rahil],
Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement,
MLMI16(254-261).
Springer DOI 1611
BibRef

Kropidlowski, K.[Karol], Kociolek, M.[Marcin], Strzelecki, M.[Michal], Czubinski, D.[Dariusz],
Blue Whitish Veil, Atypical Vascular Pattern and Regression Structures Detection in Skin Lesions Images,
ICCVG16(418-428).
Springer DOI 1611
BibRef

Sun, X.X.[Xiao-Xiao], Yang, J.F.[Ju-Feng], Sun, M.[Ming], Wang, K.[Kai],
A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images,
ECCV16(VI: 206-222).
Springer DOI 1611
BibRef

Bulan, O., Artan, Y.,
Wheal detection from skin prick test images using normalized-cuts and region selection,
ICIP16(1250-1253)
IEEE DOI 1610
Calibration BibRef

Schneider, D., Hecht, A.,
Photogrammetric 3d Acquisition And Analysis Of Medicamentous Induced Pilomotor Reflex (goose Bumps),
ISPRS16(B5: 903-908).
DOI Link 1610
BibRef

Bernart, E., Scharcanski, J., Bampi, S.,
Segmentation and classification of melanocytic skin lesions using local and contextual features,
ICIP16(2633-2637)
IEEE DOI 1610
Cancer BibRef

Mete, M., Sirakov, N.M., Griffin, J., Menter, A.,
A novel classification system for dysplastic nevus and malignant melanoma,
ICIP16(3414-3418)
IEEE DOI 1610
Lesions BibRef

Majtner, T.[Tomáš], Yildirim-Yayilgan, S.[Sule], Hardeberg, J.Y.[Jon Yngve],
Efficient Melanoma Detection Using Texture-Based RSurf Features,
ICIAR16(30-37).
Springer DOI 1608
BibRef

Akaho, R.[Rina], Hirose, M.[Misa], Tsumura, N.[Norimichi],
Nonlinear Estimation of Chromophore Concentrations and Shading from Hyperspectral Images,
ICISP16(101-108).
WWW Link. 1606
melanin, oxy-hemoglobin, deoxy-hemoglobin and shading. BibRef

Afifi, S.[Shereen], Gholam Hosseini, H.[Hamid], Sinha, R.[Roopak],
Hardware Acceleration of SVM-Based Classifier for Melanoma Images,
MCBMIIA15(235-245).
Springer DOI 1603
BibRef

Saleh, F.S., Azmi, R.,
Automated lesion border detection of dermoscopy images using spectral clustering,
IPRIA15(1-6)
IEEE DOI 1603
cancer BibRef

Hames, S.C., Ardigo, M., Soyer, H.P., Bradley, A.P., Prow, T.W.,
Anatomical Skin Segmentation in Reflectance Confocal Microscopy with Weak Labels,
DICTA15(1-8)
IEEE DOI 1603
feature extraction BibRef

Madooei, A.[Ali], Drew, M.S.[Mark S.],
Detecting specular highlights in dermatological images,
ICIP15(4357-4360)
IEEE DOI 1512
DRM BibRef

Gonzalez-Castro, V., Debayle, J., Wazaefi, Y., Rahim, M., Gaudy, C., Grob, J.J., Fertil, B.,
Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns,
ICIP15(1722-1726)
IEEE DOI 1512
General adaptive neighborhoods BibRef

Santos, A.[Anderson], Pedrini, H.[Helio],
Human Skin Segmentation Improved by Saliency Detection,
CAIP15(II:146-157).
Springer DOI 1511
BibRef

Codella, N.[Noel], Cai, J.J.[Jun-Jie], Abedini, M.[Mani], Garnavi, R.[Rahil], Halpern, A.[Alan], Smith, J.R.[John R.],
Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images,
MLMI15(118-126).
Springer DOI 1511
BibRef

Rizzi, M.[Maria], d'Aloia, M.[Matteo], Cice, G.[Gianpaolo],
Computer Aided Evaluation (CAE) of Morphologic Changes in Pigmented Skin Lesions,
ISCA15(250-257).
Springer DOI 1511
BibRef

Santos, A.[Anderson], Pedrini, H.[Helio],
Human Skin Segmentation Improved by Texture Energy Under Superpixels,
CIARP15(35-42).
Springer DOI 1511
BibRef

Kaur, P.[Parneet], Dana, K.J.[Kristin J.], Cula, G.O.[Gabriela Oana],
From photography to microbiology: Eigenbiome models for skin appearance,
BioImage15(1-10)
IEEE DOI 1510
Artificial neural networks BibRef

Gupta, M.D.[Mithun Das], Srinivasa, S.[Srinidhi], Madhukara, J., Antony, M.[Meryl],
KL divergence based agglomerative clustering for automated Vitiligo grading,
CVPR15(2700-2709)
IEEE DOI 1510
BibRef

Koehoorn, J.[Joost], Sobiecki, A.C.[André C.], Boda, D.[Daniel], Diaconeasa, A.[Adriana], Doshi, S.[Susan], Paisey, S.[Stephen], Jalba, A.[Andrei], Telea, A.[Alexandru],
Automated Digital Hair Removal by Threshold Decomposition and Morphological Analysis,
ISMM15(15-26).
Springer DOI 1506
BibRef

Wazaefi, Y., Paris, S., Fertil, B.,
Contribution of a classifier of skin lesions to the dermatologist's decision,
IPTA12(207-211)
IEEE DOI 1503
cancer BibRef

Toth, J.[Janos], Szakacs, L.[Laszlo], Hajdu, A.[Andras],
Finding the optimal parameter setting for an ensemble-based lesion detector,
ICIP14(3532-3536)
IEEE DOI 1502
Databases BibRef

Kechichian, R., Gong, H., Revenu, M., Lezoray, O., Desvignes, M.,
New data model for graph-cut segmentation: Application to automatic melanoma delineation,
ICIP14(892-896)
IEEE DOI 1502
Image color analysis BibRef

Lezoray, O., Revenu, M., Desvignes, M.,
Graph-based skin lesion segmentation of multispectral dermoscopic images,
ICIP14(897-901)
IEEE DOI 1502
Clustering algorithms BibRef

Mete, M.[Mutlu], Sirakov, N.M.[Nikolay Metodiev],
Optimal set of features for accurate skin cancer diagnosis,
ICIP14(2256-2260)
IEEE DOI 1502
Accuracy BibRef

Vasconcelos, M.J.M.[Maria João M.], Rosado, L.[Luís], Ferreira, M.[Márcia],
Principal Axes-Based Asymmetry Assessment Methodology for Skin Lesion Image Analysis,
ISVC14(II: 21-31).
Springer DOI 1501
BibRef

Satat, G., Barsi, C., Raskar, R.,
Skin perfusion photography,
ICCP14(1-8)
IEEE DOI 1411
biomedical optical imaging BibRef

Kropidlowski, K.[Karol], Kociolek, M.[Marcin], Strzelecki, M.[Michal], Czubinski, D.[Dariusz],
Model Based Approach for Melanoma Segmentation,
ICCVG14(347-355).
Springer DOI 1410
BibRef

Nasonova, A.[Alexandra], Nasonov, A.[Andrey], Krylov, A.[Andrey], Pechenko, I.[Ivan], Umnov, A.[Alexey], Makhneva, N.[Natalia],
Image Warping in Dermatological Image Hair Removal,
ICIAR14(II: 159-166).
Springer DOI 1410
BibRef

Somoza, E.[Eduardo], Cula, G.O.[Gabriela Oana], Correa, C.[Catherine], Hirsch, J.B.[Julie B.],
Automatic Localization of Skin Layers in Reflectance Confocal Microscopy,
ICIAR14(II: 141-150).
Springer DOI 1410
BibRef

Abbas, Q.[Qaisar], Fondón, I.[Irene], Sarmiento, A.[Auxiliadora], Celebi, M.E.[M. Emre],
An Improved Segmentation Method for Non-melanoma Skin Lesions Using Active Contour Model,
ICIAR14(II: 193-200).
Springer DOI 1410
BibRef

Liu, Z.[Zhao], Zerubia, J.B.[Josiane B.],
Melanin and Hemoglobin Identification for Skin Disease Analysis,
ACPR13(145-149)
IEEE DOI 1408
diseases BibRef

Ma, Z.[Zhen], Tavares, J.M.R.S.[João Manuel R. S.],
Segmentation of Skin Lesions Using Level Set Method,
CompIMAGE14(228-233).
Springer DOI 1407
BibRef

Abuzaghleh, O., Barkana, B.D., Faezipour, M.,
SKINcure: A real time image analysis system to aid in the malignant melanoma prevention and early detection,
Southwest14(85-88)
IEEE DOI 1406
cancer BibRef

Hirose, M.[Misa], Toyota, S.[Saori], Tatsuzawa, Y.[Yuri], Tsumura, N.[Norimichi],
Evaluating Visibility of Age Spot and Freckle Based on Simulated Spectral Reflectance of Skin,
ICISP14(9-17).
Springer DOI 1406
BibRef

Vasconcelos, M.J.M.[Maria João M.], Rosado, L.[Luís],
No-reference Blur Assessment of Dermatological Images Acquired via Mobile Devices,
ICISP14(350-357).
Springer DOI 1406
BibRef

Jaworek-Korjakowska, J.[Joanna], Tadeusiewicz, R.[Ryszard],
Assessment of dots and globules in dermoscopic color images as one of the 7-point check list criteria,
ICIP13(1456-1460)
IEEE DOI 1402
dots and globules;feature extraction;hair removal;melanoma;skin lesion BibRef

Seck, A.[Alassane], Dee, H.[Hannah], Tiddeman, B.[Bernard],
3D Facial Skin Texture Analysis Using Geometric Descriptors,
ICPR14(1126-1131)
IEEE DOI 1412
BibRef
Earlier:
Local Orientation Patterns for 3D Surface Texture Analysis of Normal Maps: Application to Facial Skin Condition Classification,
ISVC13(I:572-581).
Springer DOI 1310
Feature extraction BibRef

Barata, C.[Catarina], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.],
Color Detection in Dermoscopy Images Based on Scarce Annotations,
IbPRIA15(309-316).
Springer DOI 1506
BibRef

Ruela, M.[Margarida], Barata, C.[Catarina], Marques, J.S.[Jorge S.],
What Is the Role of Color Symmetry in the Detection of Melanomas?,
ISVC13(I:1-10).
Springer DOI 1310
BibRef

Ruela, M.[Margarida], Barata, C.[Catarina], Mendonça, T.[Teresa], Marques, J.S.[Jorge S.],
What Is the Role of Color in Dermoscopy Analysis?,
IbPRIA13(819-826).
Springer DOI 1307
BibRef

Ferreira, P.M.[Pedro M.], Mendonça, T.[Teresa], Rocha, P.[Paula],
A Wide Spread of Algorithms for Automatic Segmentation of Dermoscopic Images,
IbPRIA13(592-599).
Springer DOI 1307
BibRef

Pereira, C.[Carla], Veiga, D.[Diana], Mahdjoub, J.[Jason], Guessoum, Z.[Zahia], Gonçalves, L.[Luís],
Small Red Lesions Detection Using a MAS Approach,
ICIAR13(521-529).
Springer DOI 1307
BibRef

Barata, C.[Catarina], Marques, J.S.[Jorge S.], Mendonça, T.[Teresa],
Bag-of-Features Classification Model for the Diagnose of Melanoma in Dermoscopy Images Using Color and Texture Descriptors,
ICIAR13(547-555).
Springer DOI 1307
BibRef

Barata, C.[Catarina], Marques, J.S.[Jorge S.], Rozeira, J.[Jorge],
Evaluation of Color Based Keypoints and Features for the Classification of Melanomas Using the Bag-of-Features Model,
ISVC13(I:40-49).
Springer DOI 1310
BibRef
Earlier:
The Role of Keypoint Sampling on the Classification of Melanomas in Dermoscopy Images Using Bag-of-Features,
IbPRIA13(715-723).
Springer DOI 1307
BibRef

He, Y.D.[Ying-Ding], Xie, F.Y.[Feng-Ying],
Automatic Skin Lesion Segmentation Based on Texture Analysis and Supervised Learning,
ACCV12(II:330-341).
Springer DOI 1304
BibRef

Mete, M.[Mutlu], Ou, Y.L.[Ye-Lin], Sirakov, N.M.[Nikolay Metodiev],
Skin Lesion Feature Vector Space with a Metric to Model Geometric Structures of Malignancy for Classification,
IWCIA12(285-297).
Springer DOI 1211
BibRef

Razeghi, O.[Orod], Qiu, G.P.[Guo-Ping], Williams, H.[Hywel], Thomas, K.[Kim],
Computer Aided Skin Lesion Diagnosis with Humans in the Loop,
MLMI12(266-274).
Springer DOI 1211
BibRef

Oyola, J.[Julián], Arroyo, V.[Virginia], Ruedin, A.[Ana], Acevedo, D.[Daniel],
Detection of Chickenpox Vesicles in Digital Images of Skin Lesions,
CIARP12(583-590).
Springer DOI 1209
BibRef

Güçin, M., Patias, P., Altan, M.O.,
Detection and Evaluation of Skin Disorders By One of Photogrammetric Image Analysis Methods,
ISPRS12(XXXIX-B3:537-542).
DOI Link 1209
BibRef

Amelard, R.[Robert], Wong, A.[Alexander], Clausi, D.A.[David A.],
Extracting High-Level Intuitive Features (HLIF) for Classifying Skin Lesions Using Standard Camera Images,
CRV12(396-403).
IEEE DOI 1207
BibRef

Gong, H.[Hao], Desvignes, M.[Michel],
Hemoglobin and Melanin Quantification on Skin Images,
ICIAR12(II: 198-205).
Springer DOI 1206
BibRef

Ramli, R., Malik, A.S., Hani, A.F.M., Yap, F.B.,
Segmentation of Acne Vulgaris Lesions,
DICTA11(335-339).
IEEE DOI 1205
BibRef

Cavalcanti, P.G., Yari, Y., Scharcanski, J.,
Pigmented skin lesion segmentation on macroscopic images,
IVCNZ10(1-7).
IEEE DOI 1203
BibRef

Sirakov, N.M.[Nikolay Metodiev], Mete, M.[Mutlu], Chakrader, N.S.[Nara Surendra],
Automatic boundary detection and symmetry calculation in dermoscopy images of skin lesions,
ICIP11(1605-1608).
IEEE DOI 1201
BibRef

Prigent, S.[Sylvain], Zugaj, D.[Didier], Descombes, X.[Xavier], Martel, P.[Philippe], Zerubia, J.B.[Josiane B.],
Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images,
ICIP11(2801-2804).
IEEE DOI 1201
BibRef

Khak Abi, S.[Sina], Lee, T.K.[Tim K.], Atkins, M.S.[M. Stella],
Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis,
MLMI11(291-299).
Springer DOI 1109
BibRef

Babu, M.N.[M. Naresh], Madasu, V.K.[Vamsi K.], Hanmandlu, M., Vasikarla, S.,
Histo-pathological image analysis using OS-FCM and level sets,
AIPR10(1-8).
IEEE DOI 1010
Orientation Sensitive Fuzzy C-means algorithm (OS-FCM) Skin cancer, melanoma, analysis BibRef

Garnavi, R.[Rahil], Aldeen, M.[Mohammad], Bailey, J.[James],
Classification of Melanoma Lesions Using Wavelet-Based Texture Analysis,
DICTA10(75-81).
IEEE DOI 1012
BibRef

Ali, M.[Mcheik], Hadj, B.[Batatia],
Segmentation of oct skin images by classification of speckle statistical parameters,
ICIP10(613-616).
IEEE DOI 1009
OCT: Optical Coherence Tompgraphy. BibRef

Prigent, S.[Sylvain], Descombes, X.[Xavier], Zugaj, D.[Didier], Petit, L.[Laurent], Zerubia, J.B.[Josiane B.],
Multi-scale analysis of skin hyper-pigmentation evolution,
ICIP13(626-629)
IEEE DOI 1402
Diseases BibRef

Prigent, S.[Sylvain], Descombes, X.[Xavier], Zugaj, D.[Didier], Martel, P.[Philippe], Zerubia, J.B.[Josiane B.],
Multi-spectral image analysis for skin pigmentation classification,
ICIP10(3641-3644).
IEEE DOI 1009
BibRef

Lu, J.[Juan], Manton, J.H.[Jonathan H.], Kazmierczak, E.[Ed], Sinclair, R.[Rodney],
Erythema detection in digital skin images,
ICIP10(2545-2548).
IEEE DOI 1009
BibRef

Subramaniam, N.[Nitya], Saman, G.[Gule], Hancock, E.R.[Edwin R.],
Detection of skin lesions using diffuse polarisation,
ICIP10(3021-3024).
IEEE DOI 1009
BibRef

Zacher, A.[Andrzej],
Utilization of Multi-spectral Images in Photodynamic Diagnosis,
ICCVG10(II: 367-375).
Springer DOI 1009
BibRef

Zacher, A.[Andrzej],
The Spectral Analysis of Human Skin Tissue Using Multi-spectral Images,
ICCVG10(II: 376-384).
Springer DOI 1009
BibRef

Placidi, G.[Giuseppe], Cifone, M.G.[Maria Grazia], Cinque, B.[Benedetta], Franchi, D.[Danilo], Giuliani, M.[Maurizio], La Torre, C.[Cristina], Macchiarelli, G.[Guido], Maglione, M.[Marta], Maurizi, A.[Alfredo], Miconi, G.[Gianfranca], Sotgiu, A.[Antonello],
Numerical Methods for the Semi-automatic Analysis of Multimodal Wound Healing Images,
CompIMAGE10(151-162).
Springer DOI 1006
BibRef

George, Y., Aldeen, M.[Mohammad], Garnavi, R.[Rahil],
A Pixel-Based Skin Segmentation in Psoriasis Images Using Committee of Machine Learning Classifiers,
DICTA17(1-8)
IEEE DOI 1804
diseases, feature extraction, image classification, image colour analysis, image segmentation, Training BibRef

George, Y., Aldeen, M.[Mohammad], Garnavi, R.[Rahil],
Skin Hair Removal for 2D Psoriasis Images,
DICTA15(1-8)
IEEE DOI 1603
diseases BibRef

Garnavi, R.[Rahil], Aldeen, M.[Mohammad], Finch, S.[Sue], Varigos, G.[George],
Global versus Hybrid Thresholding for Border Detection in Dermoscopy Images,
ICISP10(531-540).
Springer DOI 1006
BibRef

Jha, A.K.[Abhinav K.], Kupinski, M.A.[Matthew A.], Rodriguez, J.J.[Jeffrey J.], Stephen, R.M.[Renu M.], Stopeck, A.T.[Alison T.],
ADC estimation of lesions in diffusion-weighted MR images: A Maximum-Likelihood Approach,
Southwest10(209-212).
IEEE DOI 1005
BibRef

Fadzil, M.H.A.[M.H. Ahmad], Fitriyah, H.[Hurriyatul], Prakasa, E.[Esa], Nugroho, H.[Hermawan], Hussein, S.H., Affandi, A.M.[Azura Mohammed],
Thickness Characterization of 3D Skin Surface Images Using Reference Line Construction Approach,
IVIC09(448-454).
Springer DOI 0911
3d surface analysis for skin lesions. BibRef

Hani, A.F.M.[Ahmad Fadzil M.], Eltegani, N.M.[Nejood M.], Hussein, S.H.[Suraiya H.], Jamil, A.[Adawiyah], Gill, P.[Priya],
Assessment of Ulcer Wounds Size Using 3D Skin Surface Imaging,
IVIC09(243-253).
Springer DOI 0911
BibRef

Nayak, R.[Rohit], Kumarand, P.[Pramod], Galigekere, R.R.[Ramesh R.],
Towards a comprehensive assessment of wound-composition using color-image processing,
ICIP09(4185-4188).
IEEE DOI 0911
BibRef

Mendoza, C.S.[Carlos S.], Serrano, C.[Carmen], Acha, B.[Begona],
Scale invariant descriptors in pattern analysis of melanocytic lesions,
ICIP09(4193-4196).
IEEE DOI 0911
BibRef

Clawson, K.M., Morrow, P.J., Scotney, B.W., McKenna, D.J., Dolan, O.M.,
Analysis of Pigmented Skin Lesion Border Irregularity Using the Harmonic Wavelet Transform,
IMVIP09(18-23).
IEEE DOI 0909
BibRef

Celebi, M.E.[M. Emre], Hwang, S.[Sae], Iyatomi, H.[Hitoshi], Schaefer, G.[Gerald],
Robust border detection in dermoscopy images using threshold fusion,
ICIP10(2541-2544).
IEEE DOI 1009
BibRef

Iyatomi, H.[Hitoshi], Celebi, M.E.[M. Emre], Schaefer, G.[Gerald], Tanaka, M.[Masaru],
Automated color normalization for dermoscopy images,
ICIP10(4357-4360).
IEEE DOI 1009
BibRef

Celebi, M.E.[M. Emre], Iyatomi, H.[Hitoshi], Schaefer, G.[Gerald],
Contrast enhancement in dermoscopy images by maximizing a histogram bimodality measure,
ICIP09(2601-2604).
IEEE DOI 0911
BibRef

Schaefer, G.[Gerald], Rajab, M.I.[Maher I.], Celebi, M.E.[M. Emre], Iyatomi, H.[Hitoshi],
Skin lesion extraction in dermoscopic images based on colour enhancement and iterative segmentation,
ICIP09(3361-3364).
IEEE DOI 0911
BibRef

Schaefer, G., Krawczyk, B., Celebi, M.E., Iyatomi, H.,
Melanoma Classification Using Dermoscopy Imaging and Ensemble Learning,
ACPR13(386-390)
IEEE DOI 1408
biomedical optical imaging BibRef

Celebi, M.E.[M. Emre], Iyatomi, H.[Hitoshi], Schaefer, G.[Gerald], Stoecker, W.V.[William V.],
Localization of Lesions in Dermoscopy Images Using Ensembles of Thresholding Methods,
PSIVT09(1094-1103).
Springer DOI 0901
BibRef

Madasu, V.K.[Vamsi K.], Lovell, B.C.[Brian C.],
Blotch Detection in Pigmented Skin Lesions Using Fuzzy Co-clustering and Texture Segmentation,
DICTA09(25-31).
IEEE DOI 0912
BibRef

Capdehourat, G.[Germán], Corez, A.[Andrés], Bazzano, A.[Anabella], Musé, P.[Pablo],
Pigmented Skin Lesions Classification Using Dermatoscopic Images,
CIARP09(537-544).
Springer DOI 0911
BibRef

Ma, L.[Li], Guo, A.Z.[An-Zhe], Zou, S.F.[Shao-Fang], Xu, W.D.[Wei-Dong],
Irregularity and Asymmetry Analysis of Skin Lesions Based on Multi-Scale Local Fractal Distributions,
CISP09(1-5).
IEEE DOI 0910
BibRef

Madan, S.K.[Siddharth K.], Dana, K.J.[Kristin J.], Cula, O.G.[Oana G.],
Quasiconvex alignment of multimodal skin images for quantitative dermatology,
MMBIA09(117-124).
IEEE DOI 0906
BibRef

Mirzaalian, H.[Hengameh], Lee, T.K.[Tim K.], Hamarneh, G.[Ghassan],
Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature,
MMBIA12(97-101).
IEEE DOI 1203
BibRef
Earlier: A1, A3, A2:
A graph-based approach to skin mole matching incorporating template-normalized coordinates,
CVPR09(2152-2159).
IEEE DOI 0906
BibRef

Bouhlel, N.[Nizar], Hajjaji, S.[Salwa], Sevestre, S.[Sylvie], Laugier, P.[Pascal],
Texture analysis using Nakagami-MRF model: Preliminary results on ultrasound images of primary choroidal melanomas,
ICIP09(4181-4184).
IEEE DOI 0911
BibRef

Chaudhry, M.A.[M. Ali], Ashraf, R.[Robina], Jafri, M.N., Akbar, M.,
Computer aided diagnosis of skin carcinomas based on textural characteristics,
ICMV07(125-128).
IEEE DOI 0712
BibRef

Cho, T.S.[Taeg Sang], Freeman, W.T.[William T], Tsao, H.[Hensin],
A reliable skin mole localization scheme,
MMBIA07(1-8).
IEEE DOI 0710
BibRef

Situ, N.[Ning], Yuan, X.J.[Xiao-Jing], Zouridakis, G.[George], Mullani, N.[Nizar],
Automatic Segmentation of Skin Lesion Images using Evolutionary Strategy,
ICIP07(VI: 277-280).
IEEE DOI 0709
BibRef

Clawson, K.M., Morrow, P.J., Scotney, B.W., McKenna, D.J., Dolan, O.M.,
Determination of Optimal Axes for Skin Lesion Asymmetry Quantification,
ICIP07(II: 453-456).
IEEE DOI 0709
BibRef
And:
Computerised Skin Lesion Surface Analysis for Pigment Asymmetry Quantification,
IMVIP07(75-82).
IEEE DOI 0709
BibRef

Xu, C.Z.[Cheng-Zhe], Kim, I.[Intaek], Kim, M.S.[Moon S.],
Poultry Skin Tumor Detection in Hyperspectral Reflectance Images by Combining Classifiers,
ICIAR07(1289-1296).
Springer DOI 0708
BibRef

Odeh, S.M.[Suhail M.], Ros, E.[Eduardo], Rojas, I.[Ignacio], Palomares, J.M.[Jose M.],
Skin Lesion Diagnosis Using Fluorescence Images,
ICIAR06(II: 648-659).
Springer DOI 0610
BibRef

Aribisala, B.S.[Benjamin S.], Claridge, E.[Ela],
A Border Irregularity Measure Using a Modified Conditional Entropy Method as a Malignant Melanoma Predictor,
ICIAR05(914-921).
Springer DOI 0509
BibRef

Kolesnik, M.[Marina], Fexa, A.[Ales],
Multi-dimensional Color Histograms for Segmentation of Wounds in Images,
ICIAR05(1014-1022).
Springer DOI 0509
BibRef

Kontinen, J.[Jukka], Röning, J.[Juha], MacKie, R.M.[Rona M.],
Texture features in the classification of melanocytic lesions,
CIAP97(II: 453-460).
Springer DOI 9709
BibRef

Schmid, P.[Philippe],
Lesion Detection in Dermatoscopic Images Using Anisotropic Diffusion and Morphological Flooding,
ICIP99(III:449-453).
IEEE DOI BibRef 9900

Yamada, T., Saito, H., Ozawa, S.,
3d Reconstruction of Skin Surface from Image Sequence,
MVA98(xx-yy).
See also 3D Reconstruction of Book Surface Taken from Image Sequence with Handy Camera. BibRef 9800

Roehrer, R.[Reinhard], Ganster, H.[Harald], Pinz, A.[Axel],
Feature Selection in Melanoma Recognition,
ICPR98(Vol II: 1668-1670).
IEEE DOI 9808
BibRef

Riech, M.[Marcel], Roning, J.[Juha],
Registration of NEVI in Successive Skin Images for Early Detection of Melanoma,
ICPR98(Vol I: 352-357).
IEEE DOI 9808
BibRef

Fischer, S.[Stefan], Schmid, P.[Philippe], Guillod, J.[Joel],
Analysis of Skin Lesions with Pigmented Networks,
ICIP96(I: 323-326).
IEEE DOI BibRef 9600

Kutics, A.[Andrea], Date, M.[Munehiro],
A new parallel method based on a genetic approach for determination and classification of skin spots,
CIAP95(121-126).
Springer DOI 9509
BibRef

Ross, T., Handels, H., Kreusch, J., Busche, H., Wolf, H.H., Pöppl, S.J.,
Automatic classification of skin tumours with high resolution surface profiles,
CAIP95(368-375).
Springer DOI 9509
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Endoscopy .