21.7.2.5 Mammography, Texture Based Techniques, Wavelets

Karssemeijer, N.,
Stochastic model for automated detection of calcifications in digital mammograms,
IVC(10), No. 6, July-August 1992, pp. 369-375.
Elsevier DOI 0401
BibRef

Miller, P.[Peter], Astley, S.[Sue],
Classification of Breast Tissue by Texture Analysis,
IVC(10), No. 5, June 1992, pp. 277-282.
Elsevier DOI BibRef 9206
Earlier: BMVC91(xx-yy).
PDF File. 9109
BibRef

Miller, P.[Peter], Astley, S.[Sue],
Automated detection of Breast Asymmetries,
BMVC93(xx-yy).
PDF File. 9309
BibRef

Sahiner, B., Chan, H.P.[Heang-Ping], Petrick, N., Wei, D.T.[Da-Tong], Helvie, M.A., Adler, D.D., Goodsitt, M.M.,
Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images,
MedImg(15), No. 5, October 1996, pp. 598-610.
IEEE Top Reference. 0203
BibRef

Dhawan, A.P., Chitre, Y., Kaiser-Bonasso, C.,
Analysis of mammographic microcalcifications using gray-level image structure features,
MedImg(15), No. 3, June 1996, pp. 246-259.
IEEE Top Reference. 0203
BibRef

Strickland, R.N., Hahn, H.I.[Hee Il],
Wavelet transforms for detecting microcalcifications in mammograms,
MedImg(15), No. 2, April 1996, pp. 218-229.
IEEE Top Reference. 0203
BibRef
Earlier:
Wavelet transform matched filters for the detection and classification of microcalcifications in mammography,
ICIP95(I: 422-425).
IEEE DOI 9510
BibRef
Earlier:
Wavelet transforms for detecting microcalcifications in mammography,
ICIP94(I: 402-406).
IEEE DOI 9411
BibRef

Marchette, D.J., Lorey, R.A., Priebe, C.E.,
An Analysis of Local Feature-Extraction in Digital Mammography,
PR(30), No. 9, September 1997, pp. 1547-1554.
Elsevier DOI 9708
BibRef

Heine, J.J., Deans, S.R., Cullers, D.K., Stauduhar, R., Clarke, L.P.,
Multiresolution statistical analysis of high-resolution digital mammograms,
MedImg(16), No. 5, October 1997, pp. 503-515.
IEEE Top Reference. 0205
BibRef

Kim, J.K., Park, J.M., Song, K.S., Park, H.W.,
Detection of Clustered Microcalcifications on Mammograms Using Surrounding Region Dependence Method and Artificial Neural Network,
VLSIVideo(18), No. 3, April 1998, pp. 251-262. 9806

See also Adaptive Mammographic Image Enhancement Using First Derivative and Local Statistics. BibRef

Lee, Y.J.[Yong Jin], Park, J.M.[Jeong Mi], Park, H.W.[Hyun Wook],
Mammographic mass detection by adaptive thresholding and region growing,
IJIST(11), No. 5, 2000, pp. 340-346.
WWW Link. 0110
BibRef

Kim, J.K.[Jong Kook], Park, H.W.[Hyun Wook],
Statistical textural features for detection of microcalcifications in digitized mammograms,
MedImg(18), No. 3, March 1999, pp. 231-238.
IEEE Top Reference. 0110
BibRef
Earlier:
Surrounding Region Dependence Method For Detection Of Clustered Microcalcifications on Mammograms,
ICIP97(III: 535-538).
IEEE DOI 9710
BibRef

Bruce, L.M., Adhami, R.R.,
Classifying mammographic mass shapes using the wavelet transform modulus-maxima method,
MedImg(18), No. 12, December 1999, pp. 1170-1177.
IEEE Top Reference. 0110
BibRef

Mudigonda, N.R., Rangayyan, R., Desautels, J.E.L.,
Gradient and texture analysis for the classification of mammographic masses,
MedImg(19), No. 10, October 2000, pp. 1032-1043.
IEEE Top Reference. 0110
BibRef

Mudigonda, N.R., Rangayyan, R.M., Desautels, J.E.L.,
Detection of breast masses in mammograms by density slicing and texture flow-field analysis,
MedImg(20), No. 12, December 2001, pp. 1215-1227.
IEEE Top Reference. 0201
BibRef

Blot, L.[Lilian], Davis, A.[Anne], Holubinka, M.[Mike], Martí, R.[Robert], Zwiggelaar, R.[Reyer],
Automated Quality Assurance Applied to Mammographic Imaging,
JASP(2002), No. 7, July 2002, pp. 736-745. 0208
BibRef

Marti, R., Zwiggelaar, R., Rubin, C.M.E.,
Tracking Mammographic Structures Over Time,
BMVC01(Poster Session 1).
HTML Version. University of Portsmouth 0110
BibRef

Zwiggelaar, R., Parr, T.C., and Taylor, C.J.,
Finding Orientated Line Patterns in Digital Mammographic Images,
BMVC96(Applications). 9608
University of Manchester BibRef

Zwiggelaar, R.,
Separating Background Texture and Image Structure in Mammograms,
BMVC99(Posters/Exhibition/Demos).
PDF File. BibRef 9900

Zwiggelaar, R., Astley, S.M., Boggis, C.R.M., Taylor, C.J.,
Linear Structures in Mammographic Images: Detection and Classification,
MedImg(23), No. 9, September 2004, pp. 1077-1086.
IEEE Abstract. 0409
BibRef

Astley, S.M., Taylor, C.J.,
Combining cues for mammographic abnormalities,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Parr, T.C., Zwiggelaar, R., Taylor, C.J., Astley, S.M., Boggis, C.R.M.,
Detecting Stellate Lesions in Mammograms via Statistical Models,
BMVC97(xx-yy).
HTML Version. 0209
BibRef

Rocha Ferreira, C.B.[Cristiane Bastos], Borges, D.L.[Díbio Leandro],
Analysis of mammogram classification using a wavelet transform decomposition,
PRL(24), No. 7, April 2003, pp. 973-982.
Elsevier DOI 0301
BibRef

Lemaur, G., Drouiche, K., DeConinck, J.,
Highly regular wavelets for the detection of clustered microcalcifications in mammograms,
MedImg(22), No. 3, March 2003, pp. 393-401.
IEEE Abstract. 0306
BibRef

Penedo, M., Pearlman, W.A., Tahoces, P.G., Souto, M., Vidal, J.J.,
Region-based wavelet coding methods for digital mammography,
MedImg(22), No. 10, October 2003, pp. 1288-1296.
IEEE Abstract. 0310
BibRef
And: Correction: MedImg(22), No. 12, December 2003, pp. 1575-1575.
IEEE Abstract. 0401
BibRef
Earlier:
Embedded wavelet region-based coding methods applied to digital mammography,
ICIP03(III: 197-200).
IEEE DOI 0312
BibRef

Soltanian-Zadeh, H.[Hamid], Rafiee-Rad, F.[Farshid], Pourabdollah-Nejad, S.[Siamak],
Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms,
PR(37), No. 10, October 2004, pp. 1973-1986.
Elsevier DOI 0409
BibRef

Rashed, E.A.[Essam A.], Ismail, I.A.[Ismail A.], Zaki, S.I.[Sherif I.],
Multiresolution mammogram analysis in multilevel decomposition,
PRL(28), No. 2, 15 January 2007, pp. 286-292.
Elsevier DOI 0611
Digital mammogram; Discrete wavelets transform; Features extraction; Breast cancer diagnosis BibRef

Grim, J., Somol, P., Haindl, M., Danes, J.,
Computer-Aided Evaluation of Screening Mammograms Based on Local Texture Models,
IP(18), No. 4, April 2009, pp. 765-773.
IEEE DOI 0903
BibRef

Haindl, M.[Michal], Mikes, S.[Stanislav],
Unsupervised mammograms segmentation,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Li, Y.F.[Yan-Feng], Chen, H.[Houjin], Yang, Y.Y.[Yong-Yi], Yang, N.[Na],
Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation,
PR(46), No. 3, March 2013, pp. 681-691.
Elsevier DOI 1212
Homogeneous texture; Image segmentation; Intensity deviation; Kalman filter; Mammogram; Pectoral muscle BibRef

Li, X.Z.[Xi-Zhao], Williams, S.[Simon], Bottema, M.J.[Murk J.],
Background intensity independent texture features for assessing breast cancer risk in screening mammograms,
PRL(34), No. 9, July 2013, pp. 1053-1062.
Elsevier DOI 1305
Intensity independent; Mammogram; Density classification; Breast cancer; Risk evaluation; Subspace ensemble BibRef

Li, X.Z.[Xi-Zhao], Williams, S.[Simon], Bottema, M.J.[Murk J.],
Texture and region dependent breast cancer risk assessment from screening mammograms,
PRL(36), No. 1, 2014, pp. 117-124.
Elsevier DOI 1312
Texture analysis BibRef

Li, X.Z.[Xi-Zhao], Williams, S.[Simon], Bottema, M.J.[Murk J.],
Constructing and applying higher order textons: Estimating breast cancer risk,
PR(47), No. 3, 2014, pp. 1375-1382.
Elsevier DOI 1312
Texture analysis BibRef

Prabukumar, M.[Manoharan], Prasenjit, N.[Nandi], Sangeetha, V.[Vadivelu],
Feature extraction method for breast cancer diagnosis in digital mammograms using multi-resolution transformations and SVM-fuzzy logic classifier,
IJCVR(3), No. 3, 2013, pp. 279-292.
DOI Link 1402
BibRef

Suruliandi, A., Murugeswari, G., Rani, P.A.J.[P. Arockia Jansi],
Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images,
IJIG(15), No. 01, 2015, pp. 1550001.
DOI Link 1503
BibRef

Li, Y.F.[Yan-Feng], Chen, H.[Houjin], Rohde, G.K.[Gustavo Kunde], Yao, C.[Chang], Cheng, L.[Lin],
Texton analysis for mass classification in mammograms,
PRL(52), No. 1, 2015, pp. 87-93.
Elsevier DOI 1412
Breast cancer BibRef

Peikari, M., Gangeh, M.J., Zubovits, J., Clarke, G., Martel, A.L.,
Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach,
MedImg(35), No. 1, January 2016, pp. 307-315.
IEEE DOI 1601
Dictionaries BibRef

Li, Y.F.[Yan-Feng], Chen, H.[Houjin], Wei, X.[Xueye], Peng, Y.H.[Ya-Hui], Cheng, L.[Lin],
Mass classification in mammograms based on two-concentric masks and discriminating texton,
PR(60), No. 1, 2016, pp. 648-656.
Elsevier DOI 1609
Concentric mask BibRef

Pardo, A., Real, E., Krishnaswamy, V., López-Higuera, J.M., Pogue, B.W., Conde, O.M.,
Directional Kernel Density Estimation for Classification of Breast Tissue Spectra,
MedImg(36), No. 1, January 2017, pp. 64-73.
IEEE DOI 1701
Cancer BibRef

Pardo, A.[Arturo], Streeter, S.S.[Samuel S.], Maloney, B.W.[Benjamin W.], Gutiérrez-Gutiérrez, J.A.[José A.], McClatchy, D.M.[David M.], Wells, W.A.[Wendy A.], Paulsen, K.D.[Keith D.], López-Higuera, J.M.[José M.], Pogue, B.W.[Brian W.], Conde, O.M.[Olga M.],
Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models,
MedImg(40), No. 6, June 2021, pp. 1687-1701.
IEEE DOI 2106
Optical imaging, Nonlinear optics, Biomedical optical imaging, Imaging, Adaptive optics, Pathology, Optical scattering, convolutional neural networks BibRef

Haindl, M.[Michal], Remeš, V.[Václav],
Pseudocolor enhancement of mammogram texture abnormalities,
MVA(30), No. 4, June 2019, pp. 785-794.
Springer DOI 1906
BibRef

Benhassine, N.E.[Nasser Edinne], Boukaache, A.[Abdelnour], Boudjehem, D.[Djalil],
Classification of mammogram images using the energy probability in frequency domain and most discriminative power coefficients,
IJIST(30), No. 1, 2020, pp. 45-56.
DOI Link 2002
ANN, classification, computer aided diagnosis, DCT, mammogram, Naive Bayes, SVM BibRef

Melekoodappattu, J.G.[Jayesh George], Kadan, A.B.[Anoop Balakrishnan], Anoop, V.,
Early detection of breast malignancy using wavelet features and optimized classifier,
IJIST(31), No. 3, 2021, pp. 1551-1563.
DOI Link 2108
CAD, classification, GOA, mammogram, wavelets BibRef

Gupta, V.[Vibha], Bhavsar, A.[Arnav],
Sequential Modeling of Deep Features for Breast Cancer Histopathological Image Classification,
Microscopy18(2335-23357)
IEEE DOI 1812
BibRef
Earlier:
Breast Cancer Histopathological Image Classification: Is Magnification Important?,
Microscopy17(769-776)
IEEE DOI 1709
BibRef
And:
An Integrated Multi-scale Model for Breast Cancer Histopathological Image Classification with Joint Colour-Texture Features,
CAIP17(II: 354-366).
Springer DOI 1708
Breast cancer, Convolution, Biomedical imaging, Task analysis. Cancer, Feature extraction, Image color analysis, Kernel, Measurement, Microscopy, Support vector machines. BibRef

Kulshreshtha, D., Singh, V.P., Shrivastava, A., Chaudhary, A., Srivastava, R.,
Content-based mammogram retrieval using k-means clustering and local binary pattern,
ICIVC17(634-638)
IEEE DOI 1708
Breast cancer, Feature extraction, Histograms, Image retrieval, Mammography, CAD, content-based image retrieval, kmeans clustering, local binary pattern, mammography BibRef

Busaleh, M.[Mariam], Hussain, M.[Muhammad], Aboalsamh, H.A.[Hatim A.], Zuair, M.[Mansour], Bebis, G.[George],
False Positive Reduction in Breast Mass Detection Using the Fusion of Texture and Gradient Orientation Features,
ISVC16(I: 669-678).
Springer DOI 1701
BibRef

Gao, X.L.[Xiao-Li], Wang, K.[Keju], Guo, Y.[Yanan], Yang, Z.[Zhen], Ma, Y.[Yide],
Mass Segmentation in Mammograms Based on the Combination of the Spiking Cortical Model (SCM) and the Improved CV Model,
ISVC15(II: 664-671).
Springer DOI 1601
BibRef

Vargas Cardona, H.D.[Hernán Darío], Orozco, Á.A.[Álvaro A.], Álvarez, M.A.[Mauricio A.],
Automatic Recognition of Microcalcifications in Mammography Images through Fractal Texture Analysis,
ISVC14(II: 841-850).
Springer DOI 1501
BibRef

Pourreza-Shahri, R., Saki, F., Kehtarnavaz, N., Leboulluec, P., Liu, H.,
Classification of ex-vivo breast cancer positive margins measured by hyperspectral imaging,
ICIP13(1408-1412)
IEEE DOI 1402
Fourier coefficient selection features BibRef

Li, X.Z.[Xi-Zhao], Williams, S.[Simon], Lee, G.[Gobert], Deng, M.[Min],
Computer-aided mammography classification of malignant mass regions and normal regions based on novel texton features,
ICARCV12(1431-1436).
IEEE DOI 1304
BibRef

Anitha, J., Peter, J.D.[J. Dinesh],
A wavelet based morphological mass detection and classification in mammograms,
IMVIP12(25-28).
IEEE DOI 1302
BibRef

Malar, E., Kandaswamy, A., Kirthana, S.S., Nivedhitha, D.,
A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms,
IMVIP12(65-68).
IEEE DOI 1302
BibRef

Padmanabhan, S.[Sharanya], Sundararajan, R.[Raji],
Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis,
IMVIP12(153-156).
IEEE DOI 1302
BibRef

Padmanabhan, S.[Sharanya], Sundararajan, R.[Raji],
Texture and statistical analysis of mammograms: A novel method to detect tumor in Breast Cells,
IMVIP12(157-160).
IEEE DOI 1302
BibRef

Yao, C.[Chang], Yang, Y.Y.[Yong-Yi], Chen, H.[Houjin], Jing, T.[Tao], Hao, X.L.[Xiao-Li], Bi, H.J.[Hong-Jun],
Adaptive kernel learning for detection of clustered microcalcifications in mammograms,
Southwest12(5-8).
IEEE DOI 1205
BibRef

Dellepiane, S.G.[Silvana G.], Minetti, I.[Irene], Dellepiane, S.[Sara],
A Hierarchical Classification Method for Mammographic Lesions Using Wavelet Transform and Spatial Features,
ICCVG10(I: 324-332).
Springer DOI 1009
BibRef

Jasionowska, M.[Magdalena], Przelaskowski, A.[Artur], Józwiak, R.[Rafal],
Characteristics of Architectural Distortions in Mammograms: Extraction of Texture Orientation with Gabor Filters,
ICCVG10(I: 420-430).
Springer DOI 1009
BibRef

Kostopoulos, S.[Spiros], Cavouras, D.[Dionisis], Daskalakis, A.[Antonis], Kalatzis, I.[Ioannis], Bougioukos, P.[Panagiotis], Kagadis, G.C.[George C.], Ravazoula, P.[Panagiota], Nikiforidis, G.[George],
Assessing Estrogen Receptors' Status by Texture Analysis of Breast Tissue Specimens and Pattern Recognition Methods,
CAIP07(221-228).
Springer DOI 0708
BibRef

Georgsson, F.[Fredrik], Jansson, S.[Stefan], Olsén, C.[Christina],
Fractal Analysis of Mammograms,
SCIA07(92-101).
Springer DOI 0706
BibRef

Kim, H.J.[Hyung-Jun], Kim, W.H.[Won-Ha],
Automatic Detection of Spiculated Masses Using Fractal Analysis in Digital Mammography,
CAIP05(256).
Springer DOI 0509
BibRef

Chen, Y.[Yuan], Chang, C.I.[Chein-I],
A new application of texture unit coding to mass classification for mammograms,
ICIP04(V: 3335-3338).
IEEE DOI 0505
BibRef

Yuan, X.[Xin], Shi, P.C.[Peng-Cheng],
Microcalcification detection based on localized texture comparison,
ICIP04(V: 2953-2956).
IEEE DOI 0505
BibRef

Tweed, T., Miguet, S.,
Automatic detection of regions of interest in mammographies based on a combined analysis of texture and histogram,
ICPR02(II: 448-452).
IEEE DOI 0211
BibRef

Sakellaropoulos, P., Costaridou, L., Panayiotakis, G.,
Integrating Wavelet-based Mammographic Image Visualisation on a Web Browser,
ICIP01(II: 873-876).
IEEE DOI 0108
BibRef

Bovis, K., Singh, S.,
Detection of Masses in Mammograms Using Texture Features,
ICPR00(Vol II: 267-270).
IEEE DOI 0009
BibRef

Yoshida, H., Zhang, W.[Wei], Cai, W.D.[Wei-Dong], Doi, K., Nishikawa, R.M., Giger, M.L.,
Optimizing wavelet transform based on supervised learning for detection of microcalcifications in digital mammograms,
ICIP95(III: 152-155).
IEEE DOI 9510
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Mammograms, Image Enhancement, Noise Suppression .