21.4.8 Surveys, Comparisons, Cells, DNA

Chapter Contents (Back)
Survey, DNA. Cells.

Roman-Roldan, R., Bernaolagalvan, P., Oliver, J.L.,
Application of Information-Theory to DNA-Sequence Analysis: A Review,
PR(29), No. 7, July 1996, pp. 1187-1194.
Elsevier DOI 9607
BibRef

Zhang, L., Kuljis, J., Liu, X.,
Information Visualization for DNA Microarray Data Analysis: A Critical Review,
SMC-C(38), No. 1, January 2008, pp. 42-54.
IEEE DOI 0712
BibRef


21.5 Retinal Images, Analysis of Eye, etc.

Chapter Contents (Back)
Retinal Images. Eye. Fundus Images. Medical, Applications.
See also Optic Disc Location, Optic Disc Detection.
See also Retinal Mosaic Generation.
See also Retinal Images, Angiography, Blood Vessels in the Eye.
See also Retinal Identification Systems and Tecniques.
See also Diabetic Retinopathy, Retinal Analysis Application.

FIRE Fundus Image Registration Dataset,
2016
WWW Link. Dataset, Retinal. Dataset, Registration. 1610
134 retinal image pairs and ground truth for registration.

Akita, K.[Koichiro], Kuga, H.[Hideki],
A computer method of understanding ocular fundus images,
PR(15), No. 6, 1982, pp. 431-443.
Elsevier DOI 0309
BibRef

Tamura, S.[Shinichi], Tanaka, K.[Kokichi], Ohmori, S.[Seiji], Okazaki, K.[Kozo], Okada, A.[Akira], Hoshi, M.[Mitsuru],
Semiautomatic Leakage Analyzing System for Time Series Fluorescein Ocular Fundus Angiography,
PR(16), No. 2, 1983, pp. 149-162.
Elsevier DOI 0309
BibRef

Kawai, H.[Hideo], Tamura, S.[Shinichi], Kani, K.[Kazutaka], Kariya, K.[Komyo],
Eye movement analysis system using fundus images,
PR(19), No. 1, 1986, pp. 77-84.
Elsevier DOI 0309
BibRef

Jasiobedzki, P.[Piotr], Taylor, C.J.[Chris J.], Brunt, J.N.H.[John N.H.],
Automated analysis of retinal images,
IVC(11), No. 3, April 1993, pp. 139-144.
Elsevier DOI 0401
BibRef
Earlier: A1, A2, Only: BMVC91(xx-yy).
PDF File. 9109
BibRef

Yamany, S.M., Khiani, K.J., Farag, A.A.,
Application of Neural Networks and Genetic Algorithms in the Classification of Endothelial Cells,
PRL(18), No. 11-13, November 1997, pp. 1205-1210. 9806
BibRef

Abramoff, M.D., Niessen, W.J., Viergever, M.A.,
Objective quantification of the motion of soft tissues in the orbit,
MedImg(19), No. 10, October 2000, pp. 986-995.
IEEE Top Reference. 0110
BibRef

Abramoff, M.D., Viergever, M.A.,
Computation and visualization of three-dimensional soft tissue motion in the orbit,
MedImg(21), No. 4, April 2002, pp. 296-304.
IEEE Top Reference. 0206
BibRef

Sbeh, Z.B.[Zakaria Ben], Cohen, L.D., Mimoun, G., Coscas, G.,
A new approach of geodesic reconstruction for drusen segmentation in eye fundus images,
MedImg(20), No. 12, December 2001, pp. 1321-1333.
IEEE Top Reference. 0201
BibRef

Sbeh, Z.B., Cohen, L.D., Mimoun, G., Coscas, G., Soubrane, G.,
An adaptive contrast method for segmentation of drusen,
ICIP97(I: 255-258).
IEEE DOI 9710
BibRef

Wendling, L.[Laurent], Tabbone, S.A.[Salvatore A.], Matsakis, P.[Pascal],
Fast and robust recognition of orbit and sinus drawings using histograms of forces,
PRL(23), No. 14, December 2002, pp. 1687-1693.
Elsevier DOI 0208
BibRef

Coelho, R.C.[Regina Celia], di Gesů, V.[Vito], lo Bosco, G.[Giosuč], Tanaka, J.S.[Júlia Sawaki], Valenti, C.[Cesare],
Shape-Based Features for Cat Ganglion Retinal Cells Classification,
RealTimeImg(8), No. 3, June 2002, pp. 213-226.
DOI Link 0208
BibRef

Coelho, R.C., Valenti, C., Tanaka, J.S., da Fontoura Costa, L.[Luciano],
Classification of cat ganglion retinal cells and implications for shape-function relationship,
CIAP01(517-522).
IEEE DOI 0210
BibRef

Catlin, D.[David], Dainty, C.[Christopher],
High-resolution imaging of the human retina with a Fourier deconvolution technique,
JOSA-A(19), No. 8, August 2002, pp. 1515-1523.
WWW Link. 0208
BibRef

Taleb-Ahmedand, A., Bigand, A.,
Telemedicine and fuzzy logic: application in ophthalmology,
PRL(24), No. 15, November 2003, pp. 2731-2742.
Elsevier DOI 0308
BibRef

Li, H.Q.[Hui-Qi], Chutatape, O.[Opas],
Boundary detection of optic disk by a modified ASM method,
PR(36), No. 9, September 2003, pp. 2093-2104.
Elsevier DOI 0307
BibRef

Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., Kennedy, L.,
Optic Nerve Head Segmentation,
MedImg(23), No. 2, February 2004, pp. 256-264.
IEEE Abstract. 0403

See also Measurement of Retinal Vessel Widths from Fundus Images Based on 2-D Modeling. BibRef

Berestov, A.L.[Alexander L.], Garland, H.T.[Harry T.],
Automated stereo fundus evaluation,
US_Patent6,714,672, Mar 30, 2004
WWW Link. BibRef 0403

Cano, D.[Daniel], Barbero, S.[Sergio], Marcos, S.[Susana],
Comparison of real and computer-simulated outcomes of LASIK refractive surgery,
JOSA-A(21), No. 6, June 2004, pp. 926-936.
WWW Link. 0409
BibRef

Matsopoulos, G.K., Asvestas, P.A., Mouravliansky, N.A., Delibasis, K.K.,
Multimodal registration of retinal images using self organizing maps,
MedImg(23), No. 12, December 2004, pp. 1557-1563.
IEEE Abstract. 0412
BibRef

Feng, P.[Peng], Pan, Y.J.[Ying-Jun], Wei, B.[Biao], Jin, W.[Wei], Mi, D.[Deling],
Enhancing retinal image by the Contourlet transform,
PRL(28), No. 4, 1 March 2007, pp. 516-522.
Elsevier DOI 0701
The Contourlet transform; Anisotropy and directionality; Contrast enhancement; Retinal imaging BibRef

Tobin, K.W., Chaum, E., Govindasamy, V.P., Karnowski, T.P.,
Detection of Anatomic Structures in Human Retinal Imagery,
MedImg(26), No. 12, December 2007, pp. 1729-1739.
IEEE DOI 0712
BibRef

Xue, B.[Bai], Choi, S.S.[Stacey S.], Doble, N.[Nathan], Werner, J.S.[John S.],
Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera,
JOSA-A(24), No. 5, May 2007, pp. 1364-1372.
WWW Link. 0801
BibRef

Chenegros, G.[Guillaume], Mugnier, L.M.[Laurent M.], Lacombe, F.[François], Glanc, M.[Marie],
3D phase diversity: a myopic deconvolution method for short-exposure images: application to retinal imaging,
JOSA-A(24), No. 5, May 2007, pp. 1349-1357.
WWW Link. 0801
BibRef

Li, K.Y.[Kaccie Y.], Roorda, A.[Austin],
Automated identification of cone photoreceptors in adaptive optics retinal images,
JOSA-A(24), No. 5, May 2007, pp. 1358-1363.
WWW Link. 0801
BibRef

Bueno, J.M.[Juan M.], Hunter, J.J.[Jennifer J.], Cookson, C.J.[Christopher J.], Kisilak, M.L.[Marsha L.], Campbell, M.C.W.[Melanie C. W.],
Improved scanning laser fundus imaging using polarimetry,
JOSA-A(24), No. 5, May 2007, pp. 1337-1348.
WWW Link. 0801
BibRef

Carroll, J.[Joseph], Drexler, W.[Wolfgang], Roorda, A.[Austin],
Advances in Retinal Imaging: introduction,
JOSA-A(24), No. 5, May 2007, pp. 1223-1224.
WWW Link. 0801
BibRef

Wade, N.J.[Nicholas J.],
Image, eye, and retina,
JOSA-A(24), No. 5, May 2007, pp. 1229-1249.
WWW Link. 0801
Survey, Retinal Imaging. Invited Review. BibRef

Delori, F.C.[François C.], Webb, R.H.[Robert H.], Sliney, D.H.[David H.],
Maximum permissible exposures for ocular safety (ANSI 2000), with emphasis on ophthalmic devices,
JOSA-A(24), No. 5, May 2007, pp. 1250-1265.
WWW Link. 0801
BibRef

Corbett, A.D.[Alexander D.], Wilkinson, T.D.[Timothy D.], Zhong, J.J.[Jiang J.], Diaz-Santana, L.[Luis],
Designing a holographic modal wavefront sensor for the detection of static ocular aberrations,
JOSA-A(24), No. 5, May 2007, pp. 1266-1275.
WWW Link. 0801
BibRef

Zhang, Y.H.[Yu-Hua], Roorda, A.[Austin],
Photon signal detection and evaluation in the adaptive optics scanning laser ophthalmoscope,
JOSA-A(24), No. 5, May 2007, pp. 1276-1283.
WWW Link. 0801
BibRef

Hunter, J.J.[Jennifer J.], Cookson, C.J.[Christopher J.], Kisilak, M.L.[Marsha L.], Bueno, J.M.[Juan M.], Campbell, M.C.W.[Melanie C. W.],
Characterizing image quality in a scanning laser ophthalmoscope with differing pinholes and induced scattered light,
JOSA-A(24), No. 5, May 2007, pp. 1284-1295.
WWW Link. 0801
BibRef

Wanek, J.M.[Justin M.], Mori, M.[Marek], Shahidi, M.[Mahnaz],
Effect of aberrations and scatter on image resolution assessed by adaptive optics retinal section imaging,
JOSA-A(24), No. 5, May 2007, pp. 1296-1304.
WWW Link. 0801
BibRef

Chen, D.C.[Diana C.], Jones, S.M.[Steven M.], Silva, D.A.[Dennis A.], Olivier, S.S.[Scot S.],
High-resolution adaptive optics scanning laser ophthalmoscope with dual deformable mirrors Multimedia,
JOSA-A(24), No. 5, May 2007, pp. 1305-1312.
WWW Link. 0801
BibRef

Vilupuru, A.S.[Abhiram S.], Rangaswamy, N.V.[Nalini V.], Frishman, L.J.[Laura J.], Smith III, E.L.[Earl L.], Harwerth, R.S.[Ronald S.], Roorda, A.[Austin],
Adaptive optics scanning laser ophthalmoscopy for in vivo imaging of lamina cribrosa,
JOSA-A(24), No. 5, May 2007, pp. 1417-1425.
WWW Link. 0801
BibRef

Baraas, R.C.[Rigmor C.], Carroll, J.[Joseph], Gunther, K.L.[Karen L.], Chung, M.[Mina], Williams, D.R.[David R.], Foster, D.H.[David H.], Neitz, M.[Maureen],
Adaptive optics retinal imaging reveals S-cone dystrophy in tritan color-vision deficiency,
JOSA-A(24), No. 5, May 2007, pp. 1438-1447.
WWW Link. 0801
BibRef

Dalhaus, R.N.[Rob N.], Gunther, K.L.[Karen L.],
A tritan Waldo would be easier to detect in the periphery than a red/green one: evidence from visual search,
JOSA-A(29), No. 2, February 2012, pp. A298-A305.
WWW Link. 1202
BibRef

Cideciyan, A.V.[Artur V.], Swider, M.[Malgorzata], Aleman, T.S.[Tomas S.], Roman, M.I.[Marisa I.], Sumaroka, A.[Alexander], Schwartz, S.B.[Sharon B.], Stone, E.M.[Edwin M.], Jacobson, S.G.[Samuel G.],
Reduced-illuminance autofluorescence imaging in ABCA4-associated retinal degenerations,
JOSA-A(24), No. 5, May 2007, pp. 1457-1467.
WWW Link. 0801
BibRef

Lam, B.S.Y., Yan, H.,
A Novel Vessel Segmentation Algorithm for Pathological Retina Images Based on the Divergence of Vector Fields,
MedImg(27), No. 2, February 2008, pp. 237-246.
IEEE DOI 0802
BibRef

Tang, L.[Li], Garvin, M.K., Lee, K.[Kyungmoo], Alward, W.L.W., Kwon, Y.H., Abramoff, M.D.,
Robust Multiscale Stereo Matching from Fundus Images with Radiometric Differences,
PAMI(33), No. 11, November 2011, pp. 2245-2258.
IEEE DOI 1110
Stereo for retinal images. Uses features. BibRef

Sukkaew, L.[Lassada], Uyyanonvara, B.[Bunyarit], Makhanov, S.S.[Stanislav S.], Barman, S.[Sarah], Pangputhipong, P.[Pannet],
Automatic Tortuosity-Based Retinopathy of Prematurity Screening System,
IEICE(E91-D), No. 12, December 2008, pp. 2868-2874.
DOI Link 0804
BibRef

Nagayoshi, H.[Hiroto], Hiramatsu, Y.[Yoshitaka], Sako, H.[Hiroshi], Himaga, M.[Mitsutoshi], Kato, S.[Satoshi],
Detection of Fundus Lesions Using Classifier Selection,
IEICE(E92-D), No. 5, May 2009, pp. 1168-1176.
WWW Link. 0907
BibRef

Moscaritolo, M., Jampel, H., Knezevich, F., Zeimer, R.,
An Image Based Auto-Focusing Algorithm for Digital Fundus Photography,
MedImg(28), No. 11, November 2009, pp. 1703-1707.
IEEE DOI 0911
BibRef

Nair, G.[Govind], Shen, Q.A.[Qi-Ang], Duong, T.Q.[Timothy Q.],
Relaxation time constants and apparent diffusion coefficients of rat retina at 7 Tesla,
IJIST(20), No. 2, June 2010, pp. 126-130.
DOI Link 1006
MRI analysis of retina. BibRef

Quellec, G., Russell, S.R., Abramoff, M.D.,
Optimal Filter Framework for Automated, Instantaneous Detection of Lesions in Retinal Images,
MedImg(30), No. 2, February 2011, pp. 523-533.
IEEE DOI 1102
BibRef

Kim, S.K., Kim, D.M., Suh, M.H., Kim, M., Kim, H.C.,
Retinal Oximetry Based on Nonsimultaneous Image Acquisition Using a Conventional Fundus Camera,
MedImg(30), No. 8, August 2011, pp. 1577-1580.
IEEE DOI 1108
BibRef

Niemeijer, M., Xu, X., Dumitrescu, A.V., Gupta, P., van Ginneken, B., Folk, J.C., Abramoff, M.D.,
Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs,
MedImg(30), No. 11, November 2011, pp. 1941-1950.
IEEE DOI 1111
BibRef

Nieto, A.[Alejandro], Brea, V.[Victor], Vilarińo, D.L.[David L.], Osorio, R.R.[Roberto R.],
Performance analysis of massively parallel embedded hardware architectures for retinal image processing,
JIVP(2011), No. 1 2011, pp. xx-yy.
DOI Link 1203
BibRef

Nieto, A., Vilarino, D.L., Brea, V.M.,
Feature detection and matching on an SIMD/MIMD hybrid embedded processor,
ECVW12(21-26).
IEEE DOI 1207
BibRef

Chen, X., Niemeijer, M., Zhang, L., Lee, K., Abramoff, M.D., Sonka, M.,
Three-Dimensional Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search-Graph-Cut,
MedImg(31), No. 8, August 2012, pp. 1521-1531.
IEEE DOI 1208
BibRef

Welfer, D.[Daniel], Scharcanski, J.[Jacob], Marinho, D.R.[Diane Ruschel],
A morphologic two-stage approach for automated optic disk detection in color eye fundus images,
PRL(34), No. 5, 1 April 2013, pp. 476-485.
Elsevier DOI 1303
Mathematical morphology; Color images; Optic disk detection; Eye fundus image BibRef

Ghassabi, Z.[Zeinab], Sedaghat, A.[Amin], Shanbehzadeh, J.[Jamshid], Fatemizadeh, E.[Emad],
An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors,
JIVP(2013), No. 1, 2013, pp. 25.
DOI Link 1305
BibRef

Nath, M.K.[Malaya Kumar], Dandapat, S.[Samarendra],
Multiscale ICA for fundus image analysis,
IJIST(23), No. 4, 2013, pp. 327-337.
DOI Link 1312
independent component analysis BibRef

Alipour, S.H.M.[Shirin Hajeb Mohammad], Rabbani, H.[Hossein], Akhlaghi, M.[Mohammadreza],
A new combined method based on curvelet transform and morphological operators for automatic detection of foveal avascular zone,
SIViP(8), No. 2, February 2014, pp. 205-222.
Springer DOI 1402
BibRef

Dashtbozorg, B.[Behdad], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images,
IP(23), No. 3, March 2014, pp. 1073-1083.
IEEE DOI 1403
BibRef
Earlier:
Automatic Estimation of the Arteriolar-to-Venular Ratio in Retinal Images Using a Graph-Based Approach for Artery/Vein Classification,
ICIAR13(530-538).
Springer DOI 1307
blood vessels BibRef

Dashtbozorg, B.[Behdad], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
Assessment of Retinal Vascular Changes Through Arteriolar-to-Venular Ratio Calculation,
ICIAR15(335-343).
Springer DOI 1507
BibRef

Mendonça, A.M.[Ana Maria], Cardoso, F.[Filipe], Sousa, A.V.[António V.], Campilho, A.[Aurélio],
Automatic Localization of the Optic Disc in Retinal Images Based on the Entropy of Vascular Directions,
ICIAR12(II: 424-431).
Springer DOI 1206
BibRef

Marrugo, A.G.[Andres Guillermo], Millan, M.S.[Maria Sagrario],
Retinal Image Analysis Oriented to the Clinical Task,
ELCVIA(13), No. 2, 2014, pp. xx-yy.
DOI Link 1407
Ph.D.. Thesis. BibRef

Ghannoum, A., Ghafar-Zadeh, E., Sawan, M.,
Image processing system dedicated to a visual intra-cortical stimulator,
IET-IPR(8), No. 12, 2014, pp. 846-855.
DOI Link 1412
Microstimulation to counter visual impairment. Gaussian processes. BibRef

Mariotti, L.[Letizia], Devaney, N.[Nicholas],
Performance analysis of cone detection algorithms,
JOSA-A(32), No. 4, April 2015, pp. 497-506.
DOI Link 1504
Active or adaptive optics; Image processing; Ophthalmology BibRef

Muangnak, N., Aimmanee, P., Makhanov, S., Uyyanonvara, B.,
Vessel transform for automatic optic disk detection in retinal images,
IET-IPR(9), No. 9, 2015, pp. 743-750.
DOI Link 1509
biomedical optical imaging BibRef

Mayrhofer-Reinhartshuber, M.[Michael], Cornforth, D.J.[David J.], Ahammer, H.[Helmut], Jelinek, H.F.[Herbert F.],
Multiscale analysis of tortuosity in retinal images using wavelets and fractal methods,
PRL(68, Part 1), No. 1, 2015, pp. 132-138.
Elsevier DOI 1512
Retina BibRef

Lazareva, A.[Anfisa], Liatsis, P.[Panos], Rauscher, F.G.[Franziska G.],
Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images,
JOSA-A(33), No. 1, January 2016, pp. 84-94.
DOI Link 1601
BibRef
Earlier:
An automated image processing system for the detection of photoreceptor cells in adaptive optics retinal images,
WSSIP15(196-199)
IEEE DOI 1603
adaptive optics Digital image processing BibRef

Rahman, R., Kabir, S.M.R.[S. M. Raiyan], Quadir, A.[Anita],
Application of fuzzy inference and active contour model for detection of fovea and its center in a fundus image,
SIViP(10), No. 1, February 2016, pp. 397-404.
WWW Link. 1601
BibRef

Wang, S., Jin, K., Lu, H., Cheng, C., Ye, J., Qian, D.,
Human Visual System-Based Fundus Image Quality Assessment of Portable Fundus Camera Photographs,
MedImg(35), No. 4, April 2016, pp. 1046-1055.
IEEE DOI 1604
biomedical optical imaging BibRef

Coudrillier, B., Geraldes, D.M., Vo, N.T., Atwood, R., Reinhard, C., Campbell, I.C., Raji, Y., Albon, J., Abel, R.L., Ethier, C.R.,
Phase-Contrast Micro-Computed Tomography Measurements of the Intraocular Pressure-Induced Deformation of the Porcine Lamina Cribrosa,
MedImg(35), No. 4, April 2016, pp. 988-999.
IEEE DOI 1604
biological tissues BibRef

Garcia-Guerra, C.E.[Carlos E.], Aldaba, M.[Mikel], Arjona, M.[Montserrat], Diaz-Douton, F.[Fernando], Martinez-Roda, J.A.[Joan A.], Pujol, J.[Jaume],
Response for light scattered in the ocular fundus from double-pass and Hartmann-Shack estimations,
JOSA-A(33), No. 11, November 2016, pp. 2150-2157.
DOI Link 1609
Visual optics, metrology ; Visual optics, modeling BibRef

ter Haar Romeny, B.M.[Bart M.], Bekkers, E.J.[Erik J.], Zhang, J.[Jiong], Abbasi-Sureshjani, S.[Samaneh], Huang, F.[Fan], Duits, R.[Remco], Dashtbozorg, B.[Behdad], Berendschot, T.T.J.M.[Tos T. J. M.], Smit-Ockeloen, I.[Iris], Eppenhof, K.A.J.[Koen A. J.], Feng, J.H.[Jing-Han], Hannink, J.[Julius], Schouten, J.[Jan], Tong, M.M.[Meng-Meng], Wu, H.H.[Han-Hui], van Triest, H.W.[Han W.], Zhu, S.S.[Shan-Shan], Chen, D.[Dali], He, W.[Wei], Xu, L.[Ling], Han, P.[Ping], Kang, Y.[Yan],
Brain-inspired algorithms for retinal image analysis,
MVA(27), No. 8, November 2016, pp. 1117-1135.
Springer DOI 1612
BibRef

Manivannan, S., Cobb, C., Burgess, S., Trucco, E.,
Subcategory Classifiers for Multiple-Instance Learning and Its Application to Retinal Nerve Fiber Layer Visibility Classification,
MedImg(36), No. 5, May 2017, pp. 1140-1150.
IEEE DOI 1705
Biomarkers, Biomedical optical imaging, Dementia, Optical fibers, Optical imaging, Retina, Retinopathy, Image classification, multiple-instance learning(MIL), retinal biomarkers for dementia, retinal image processing, retinal, nerve, fiber, layer, (RNFL) BibRef

Novosel, J., Yzer, S., Vermeer, K.A., van Vliet, L.J.,
Segmentation of Locally Varying Numbers of Outer Retinal Layers by a Model Selection Approach,
MedImg(36), No. 6, June 2017, pp. 1306-1315.
IEEE DOI 1706
Biomarkers, Complexity theory, Data models, Diseases, Image segmentation, Maximum likelihood estimation, Retina, Akaike information criteria, Bayesian information criteria, attenuation coefficient, information complexity, maximum likelihood estimation, model selection, retinitis pigmentosa BibRef

Soomro, T.A.[Toufique Ahmed], Khan, M.A.U.[Mohammad A. U.], Gao, J.B.[Jun-Bin], Khan, T.M.[Tariq M.], Paul, M.[Manoranjan],
Contrast normalization steps for increased sensitivity of a retinal image segmentation method,
SIViP(11), No. 8, November 2017, pp. 1509-1517.
Springer DOI 1710
BibRef

Bekkers, E.J.[Erik J.], Loog, M.[Marco], ter Haar Romeny, B.M.[Bart M.], Duits, R.[Remco],
Template Matching via Densities on the Roto-Translation Group,
PAMI(40), No. 2, February 2018, pp. 452-466.
IEEE DOI 1801
BibRef
Earlier: A1, A4, A2, Only:
Training of Templates for Object Recognition in Invertible Orientation Scores: Application to Optic Nerve Head Detection in Retinal Images,
EMMCVPR15(464-477).
Springer DOI 1504
BibRef
Earlier: A1, A4, A3, Only:
Optic Nerve Head Detection via Group Correlations in Multi-orientation Transforms,
ICIAR14(II: 293-302).
Springer DOI 1410
Linear regression, Pattern matching, Retina, Smoothing methods, Splines (mathematics), Wavelet transforms, Template matching, retina BibRef

Abbasi-Sureshjani, S.[Samaneh], Smit-Ockeloen, I.[Iris], Zhang, J.[Jiong], ter Haar Romeny, B.M.[Bart M.],
Biologically-Inspired Supervised Vasculature Segmentation in SLO Retinal Fundus Images,
ICIAR15(325-334).
Springer DOI 1507
BibRef

Abdel-Hamid, L.[Lamiaa], El-Rafei, A.[Ahmed], El-Ramly, S.[Salwa], Michelson, G.[Georg],
Performance dependency of retinal image quality assessment algorithms on image resolution: analyses and solutions,
SIViP(12), No. 1, January 2018, pp. 9-16.
Springer DOI 1801
BibRef

Saha, S.[Sajib], Fletcher, A.[Alexander], Xiao, D.[Di], Kanagasingam, Y.[Yogesan],
A novel method for automated correction of non-uniform/poor illumination of retinal images without creating false artifacts,
JVCIR(51), 2018, pp. 95-103.
Elsevier DOI 1802
Color fundus image, Color correction, Illumination correction, Automated analysis of retinal image BibRef

Zhao, H., Li, H., Maurer-Stroh, S., Guo, Y., Deng, Q., Cheng, L.,
Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis,
MedImg(38), No. 1, January 2019, pp. 46-56.
IEEE DOI 1901
Image segmentation, Training, Logic gates, Generators, Retina, Machine learning, Biomedical optical imaging, phantoms BibRef

Hu, J., Chen, Y., Zhong, J., Ju, R., Yi, Z.,
Automated Analysis for Retinopathy of Prematurity by Deep Neural Networks,
MedImg(38), No. 1, January 2019, pp. 269-279.
IEEE DOI 1901
Diseases, Feature extraction, Neural networks, Retina, Pediatrics, Biomedical imaging, Computer architecture, medical image analysis BibRef

Palanisamy, G.[Gopinath], Ponnusamy, P.[Palanisamy], Gopi, V.P.[Varun P.],
An improved luminosity and contrast enhancement framework for feature preservation in color fundus images,
SIViP(13), No. 4, June 2019, pp. 719-726.
Springer DOI 1906
BibRef

Wang, R.Z.[Ren-Zhen], Chen, B.Z.[Ben-Zhi], Meng, D.Y.[De-Yu], Wang, L.S.[Li-Sheng],
Weakly Supervised Lesion Detection From Fundus Images,
MedImg(38), No. 6, June 2019, pp. 1501-1512.
IEEE DOI 1906
Lesions, Retina, Noise measurement, Diseases, Biomedical imaging, Task analysis, Blood vessels, Computer-aided detection, mixture of Gaussian BibRef

Wei, Q.J.[Qi-Jie], Li, X.R.[Xi-Rong], Wang, H.[Hao], Ding, D.Y.[Da-Yong], Yu, W.H.[Wei-Hong], Chen, Y.X.[You-Xin],
Laser Scar Detection in Fundus Images Using Convolutional Neural Networks,
ACCV18(IV:191-206).
Springer DOI 1906
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RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology,
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IEEE DOI 1908
Retina, Principal component analysis, Dimensionality reduction, Optimization, Feature extraction, Imaging, Optics, health monitoring BibRef

Brancati, N.[Nadia], Frucci, M.[Maria], Riccio, D.[Daniel], di Perna, L.[Luigi], Simonelli, F.[Francesca],
Segmentation of Pigment Signs in Fundus Images for Retinitis Pigmentosa Analysis by Using Deep Learning,
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Springer DOI 1909
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Linares, O.C., Hamann, B., Neto, J.B.[J. Batista],
Segmenting Cellular Retinal Images by Optimizing Super-Pixels, Multi-Level Modularity, and Cell Boundary Representation,
IP(29), No. , 2020, pp. 809-818.
IEEE DOI 1910
Image segmentation, Retina, Clustering algorithms, Optimization, Microscopy, Image edge detection, Method of moments, retina BibRef

Playout, C., Duval, R., Cheriet, F.,
A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images,
MedImg(38), No. 10, October 2019, pp. 2434-2444.
IEEE DOI 1910
Lesions, Retina, Image segmentation, Diseases, Training, Task analysis, Feature extraction, Computer-aided diagnostic, fundus imaging, screening BibRef

Zhao, H.[He], Li, H.Q.[Hui-Qi], Cheng, L.[Li],
Improving retinal vessel segmentation with joint local loss by matting,
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Elsevier DOI 1911
Vessel segmentation, Retinal images, Deep learning, Local matting loss BibRef

Lin, H., Li, Z., Shih, M., Sun, Y., Shen, T.,
Pupil Localization for Ophthalmic Diagnosis Using Anchor Ellipse Regression,
MVA19(1-5)
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convolutional neural nets, eye, image segmentation, infrared imaging, learning (artificial intelligence), Deep learning BibRef

Ghosh, S.K.[Swarup Kr], Biswas, B.[Biswajit], Ghosh, A.[Anupam],
SDCA: a novel stack deep convolutional autoencoder- an application on retinal image denoising,
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Hu, X.Q.[Xin-Qi], Yang, Q.A.[Qi-Ang],
Modeling and optimization of closed-loop retinal motion tracking in scanning light ophthalmoscopy,
JOSA-A(36), No. 5, May 2019, pp. 716-721.
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Adaptive optics, Eye models, Eye movements, Image metrics, Optical coherence tomography, Power spectral density BibRef

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Deep Retinal Image Segmentation With Regularization Under Geometric Priors,
IP(29), 2020, pp. 2552-2567.
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Image segmentation, Task analysis, Retinal vessels, Deep learning, Training, Feature extraction, Retinal images, deep learning, priors BibRef

Jain, P.[Prakhar], Bauskar, S.[Shubham], Gyanchandani, M.[Manasi],
Neural network based non-invasive method to detect anemia from images of eye conjunctiva,
IJIST(30), No. 1, 2020, pp. 112-125.
DOI Link 2002
anemia detection, artificial neural network, backpropagation rules, hyper parameters tuning, image augmentation BibRef

Chen, B.Z.[Ben-Zhi], Wang, L.S.[Li-Sheng], Wang, X.Y.[Xiu-Ying], Sun, J.[Jian], Huang, Y.J.[Yi-Jie], Feng, D.D.[David Dagan], Xu, Z.B.[Zong-Ben],
Abnormality detection in retinal image by individualized background learning,
PR(102), 2020, pp. 107209.
Elsevier DOI 2003
Retinal abnormality detection, Retinal lesion detection, Computer-aided detection, Dictionary learning, Retinal image reading BibRef

Keerthiveena, B., Esakkirajan, S., Selvakumar, K., Yogesh, T.,
Computer-aided diagnosis of retinal diseases using multidomain feature fusion,
IJIST(30), No. 2, 2020, pp. 367-379.
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feature fusion, intensity hue saturation, support vector machine, wavelet packet transform, weighted principal component analysis BibRef

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IET-CV(14), No. 4, June 2020, pp. 144-153.
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Li, X., Jia, M., Islam, M.T., Yu, L., Xing, L.,
Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis,
MedImg(39), No. 12, December 2020, pp. 4023-4033.
IEEE DOI 2012
Diseases, Image color analysis, Task analysis, Retina, Medical diagnosis, Photography, Learning systems, multi-modal data BibRef

Wang, S., Yu, L., Li, K., Yang, X., Fu, C.W., Heng, P.A.,
DoFE: Domain-Oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets,
MedImg(39), No. 12, December 2020, pp. 4237-4248.
IEEE DOI 2012
Image segmentation, Training, Feature extraction, Task analysis, Biomedical optical imaging, Optical imaging, feature embedding BibRef

Cao, Z.[Zheng], Sun, C.B.[Chuan-Bin], Wang, W.Z.[Wen-Zhe], Zheng, X.S.[Xiang-Shang], Wu, J.[Jian], Gao, H.H.[Hong-Hao],
Multi-modality fusion learning for the automatic diagnosis of optic neuropathy,
PRL(142), 2021, pp. 58-64.
Elsevier DOI 2101
Multi-modality, Deep learning, Optic neuropathy, Computer-aided diagnosis BibRef

Labounkova, I., Labounek, R., Nestrasil, I., Odstrcilik, J., Tornow, R.P., Kolar, R.,
Blind Source Separation of Retinal Pulsatile Patterns in Optic Nerve Head Video-Recordings,
MedImg(40), No. 3, March 2021, pp. 852-864.
IEEE DOI 2103
Retina, Hemodynamics, Biomedical optical imaging, Integrated optics, Veins, Optical imaging, Blood, spontaneous venous pulsations BibRef

Shen, Z., Fu, H., Shen, J., Shao, L.,
Modeling and Enhancing Low-Quality Retinal Fundus Images,
MedImg(40), No. 3, March 2021, pp. 996-1006.
IEEE DOI 2103
Degradation, Training, Image segmentation, Image analysis, Uncertainty, Retina, Medical diagnostic imaging, deep neural network BibRef

Bala, A.A.[A Anilet], Priya, P.A.[P Aruna], Maik, V.[Vivek],
Retinal image enhancement using adaptive histogram equalization tuned with nonsimilar grouping curvelet,
IJIST(31), No. 2, 2021, pp. 1050-1064.
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contrast-limited adaptive histogram equalization, curvelet transform, denoising, retinal image BibRef

Keerthiveena, B., Esakkirajan, S., Subudhi, B.N.[Badri Narayan], Veerakumar, T.,
A hybrid BPSO-SVM for feature selection and classification of ocular health,
IET-IPR(15), No. 2, 2021, pp. 542-555.
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Si, Z.[Ze], Fu, D.M.[Dong-Mei], Liu, Y.[Yang], Huang, Z.C.[Zhi-Cheng],
Hard exudate segmentation in retinal image with attention mechanism,
IET-IPR(15), No. 3, 2021, pp. 587-597.
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Touahri, R.[Radia], Azizi, N.[Nabiha], Hammami, N.E.[Nacer Eddine], Aldwairi, M.[Monther], Benzebouchi, N.E.[Nacer Eddine], Moumene, O.[Ouided],
Multi source retinal fundus image classification using convolution neural networks fusion and Gabor-based texture representation,
IJCVR(11), No. 4, 2021, pp. 401-428.
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Lv, Y.[Yan], Ma, H.[Hui], Li, J.[Jianian], Liu, S.C.[Shuang-Cai],
Fusing dense and ReZero residual networks for super-resolution of retinal images,
PRL(149), 2021, pp. 120-129.
Elsevier DOI 2108
Retinal images, Super-resolution reconstruction, Dense and ReZero residual networks BibRef

Li, X.M.[Xiao-Meng], Hu, X.W.[Xiao-Wei], Qi, X.J.[Xiao-Juan], Yu, L.Q.[Le-Quan], Zhao, W.[Wei], Heng, P.A.[Pheng-Ann], Xing, L.[Lei],
Rotation-Oriented Collaborative Self-Supervised Learning for Retinal Disease Diagnosis,
MedImg(40), No. 9, September 2021, pp. 2284-2294.
IEEE DOI 2109
Task analysis, Retina, Diseases, Medical diagnosis, Medical diagnostic imaging, Feature extraction, Annotations, retinal disease classification BibRef

Ju, L.[Lie], Wang, X.[Xin], Zhao, X.[Xin], Bonnington, P.[Paul], Drummond, T.[Tom], Ge, Z.Y.[Zong-Yuan],
Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling,
MedImg(40), No. 10, October 2021, pp. 2911-2925.
IEEE DOI 2110
Retina, Diseases, Imaging, Task analysis, Training, Generative adversarial networks, ultra-widefield fundus images BibRef

Zhang, J.K.[Jun-Kang], Wang, Y.Q.[Yi-Qian], Dai, J.[Ji], Cavichini, M.[Melina], Bartsch, D.U.G.[Dirk-Uwe G.], Freeman, W.R.[William R.], Nguyen, T.Q.[Truong Q.], An, C.[Cheolhong],
Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks,
IP(31), 2022, pp. 823-838.
IEEE DOI 2201
Retina, Image segmentation, Training, Convolutional neural networks, Transformers, Pipelines, convolutional neural networks BibRef

Ju, L.[Lie], Wang, X.[Xin], Wang, L.[Lin], Mahapatra, D.[Dwarikanath], Zhao, X.[Xin], Zhou, Q.[Quan], Liu, T.L.[Tong-Liang], Ge, Z.Y.[Zong-Yuan],
Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation,
MedImg(41), No. 6, June 2022, pp. 1533-1546.
IEEE DOI 2206
Noise measurement, Uncertainty, Training, Medical services, Estimation, Medical diagnostic imaging, Diseases, Label noise, retinal diseases BibRef

Pal, M.N.[Mahua Nandy], Banerjee, M.[Minakshi], Sarkar, A.[Ankit],
Quantum neural network application for exudate affected retinal image patch identification,
IJCVR(12), No. 4, 2022, pp. 360-376.
DOI Link 2207
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Meng, Q.[Qier], Liao, L.[Liang], Satoh, S.[Shin'Ichi],
Weakly-Supervised Learning With Complementary Heatmap for Retinal Disease Detection,
MedImg(41), No. 8, August 2022, pp. 2067-2078.
IEEE DOI 2208
Heating systems, Lesions, Diseases, Retina, Annotations, Training, Image segmentation, Lesion detection, Grad-CAM, complementary heatmap BibRef

Wen, Y.[Yang], Chen, L.T.[Lei-Ting], Qiao, L.F.[Li-Feng], Deng, Y.[Yu], Chen, H.S.[Hai-Sheng], Zhang, T.[Tian], Zhou, C.[Chuan],
FLeak-Seg: Automated Fundus Fluorescein Leakage Segmentation via Cross-Modal Attention Learning,
MultMedMag(29), No. 2, April 2022, pp. 114-123.
IEEE DOI 2208
Image segmentation, Linguistics, Visualization, Optical imaging, Feature extraction, Biomedical optical imaging, Training data, Attention learning BibRef

Pappu, G.P.[Geetha Pavani], Krishna, T.[Talabhakthula], Biswal, B.[Birendra], Karn, P.K.[Prakash Kumar], Biswal, P.K.[Pradyut Kumar.], Hasan, S.[Shazia], Nayak, D.[Debasish],
A deeply supervised maximum response texton based SegNet for simultaneous multi retinal lesion segmentation,
IJIST(32), No. 5, 2022, pp. 1709-1726.
DOI Link 2209
diabetic retinopathy, IDRiD dataset, maximum response texton filter bank, multi retinal lesion segmentation BibRef

Coppin, T.[Thomas], Palmer, D.W.[Douglas W.], Rana, K.[Krishan], Dansereau, D.G.[Donald G.], Collins, M.J.[Michael J.], Atchison, D.A.[David A.], Roberts, J.[Jonathan], Crawford, R.[Ross], Jaiprakash, A.[Anjali],
Design of a focused light field fundus camera for retinal imaging,
SP:IC(109), 2022, pp. 116869.
Elsevier DOI 2210
Light field imaging, Optical design, Retinal imaging BibRef

Zhu, M., Zeng, K., Lin, G., Gong, Y., Hao, T., Wattanachote, K., Luo, X.,
IterNet++: An improved model for retinal image segmentation by curvelet enhancing, guided filtering, offline hard-sample mining, and test-time augmenting,
IET-IPR(16), No. 13, 2022, pp. 3617-3633.
DOI Link 2210
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Yang, B.Y.[Bing-Yu], Zhao, H.[He], Cao, L.C.[Lv-Chen], Liu, H.R.[Han-Ruo], Wang, N.L.[Ning-Li], Li, H.Q.[Hui-Qi],
Retinal image enhancement with artifact reduction and structure retention,
PR(133), 2023, pp. 108968.
Elsevier DOI 2210
Retinal image enhancement, Generative adversarial networks, High frequency BibRef

He, A.[Along], Wang, K.[Kai], Li, T.[Tao], Bo, W.[Wang], Kang, H.[Hong], Fu, H.Z.[Hua-Zhu],
Progressive Multiscale Consistent Network for Multiclass Fundus Lesion Segmentation,
MedImg(41), No. 11, November 2022, pp. 3146-3157.
IEEE DOI 2211
Lesions, Image segmentation, Feature extraction, Task analysis, Optical imaging, Biomedical optical imaging, Semantics, consistent multi-scale BibRef

Mahapatra, S.[Sakambhari], Agrawal, S.[Sanjay],
An optimal statistical feature-based transformation function for enhancement of retinal images using adaptive enhanced leader particle swarm optimization,
IJIST(32), No. 6, 2022, pp. 2163-2183.
DOI Link 2212
enhancement, intensity transformation, PSO, retinal image BibRef

Sundar, S.[Sumod], Sumathy, S.[Subramanian],
An effective deep learning model for grading abnormalities in retinal fundus images using variational auto-encoders,
IJIST(33), No. 1, 2023, pp. 92-107.
DOI Link 2301
Diabetic macular edema, Diabetic retinopathy, Region proposal network, retinal image grading, variational auto-encoder BibRef

Liu, Q.[Qing], Liu, H.T.[Hao-Tian], Ke, W.[Wei], Liang, Y.X.[Yi-Xiong],
Automated lesion segmentation in fundus images with many-to-many reassembly of features,
PR(136), 2023, pp. 109191.
Elsevier DOI 2301
Feature reassembly, Upsampling operator, Downsampling operator, Lesion segmentation, Fundus image analysis BibRef

Zhang, H.T.[Hao-Tian], Jia, N.[Ning], Zhuo, K.Q.[Ke-Qiang], Zhao, W.D.[Wei-Dong],
Retinal fundus image registration framework using Bayesian integration and asymmetric Gaussian mixture model,
IJIST(33), No. 1, 2023, pp. 403-418.
DOI Link 2301
asymmetric Gaussian mixture model, Bayesian integration, retinal image registration framework BibRef

Xiang, D.[Dehui], Yan, S.S.[Shen-Shen], Guan, Y.[Ying], Cai, M.[Mulin], Li, Z.Q.[Zhe-Qing], Liu, H.Y.[Hai-Yun], Chen, X.J.[Xin-Jian], Tian, B.[Bei],
Semi-Supervised Dual Stream Segmentation Network for Fundus Lesion Segmentation,
MedImg(42), No. 3, March 2023, pp. 713-725.
IEEE DOI 2303
Image segmentation, Retina, Lesions, Feature extraction, Fuses, Streaming media, Training, Semi-supervised learning, optical coherence tomography BibRef

Du, Y.C.[Yu-Chen], Wang, L.S.[Li-Sheng], Meng, D.Y.[De-Yu], Chen, B.Z.[Ben-Zhi], An, C.Y.[Cheng-Yang], Liu, H.[Hao], Liu, W.P.[Wei-Ping], Xu, Y.P.[Yu-Peng], Fan, Y.[Ying], Feng, D.D.[David Dagan], Wang, X.Y.[Xiu-Ying], Xu, X.[Xun],
Individualized Statistical Modeling of Lesions in Fundus Images for Anomaly Detection,
MedImg(42), No. 4, April 2023, pp. 1185-1196.
IEEE DOI 2304
Lesions, Image reconstruction, Anomaly detection, Adaptation models, Measurement, Solid modeling, Sociology, normal personalized variations BibRef

Liu, R.[Ruhan], Wang, T.Q.[Tian-Qin], Li, H.[Huating], Zhang, P.[Ping], Li, J.[Jing], Yang, X.K.[Xiao-Kang], Shen, D.G.[Ding-Gang], Sheng, B.[Bin],
TMM-Nets: Transferred Multi- to Mono-Modal Generation for Lupus Retinopathy Diagnosis,
MedImg(42), No. 4, April 2023, pp. 1083-1094.
IEEE DOI 2304
Lesions, Transfer learning, Retinopathy, Image synthesis, Training, Data models, Biomedical imaging, Lupus retinopathy, unmatched multi-modal data BibRef

Guo, E.[Erjian], Fu, H.Z.[Hua-Zhu], Zhou, L.P.[Lu-Ping], Xu, D.[Dong],
Bridging Synthetic and Real Images: A Transferable and Multiple Consistency Aided Fundus Image Enhancement Framework,
MedImg(42), No. 8, August 2023, pp. 2189-2199.
IEEE DOI 2308
Retina, Image enhancement, Task analysis, Adaptation models, Deep learning, Transformers, Computer architecture, Fundus image, image enhancement BibRef

Chen, S.[Shaobin], Wu, Z.[Zhenquan], Li, M.Z.[Ming-Zhu], Zhu, Y.[Yun], Xie, H.[Hai], Yang, P.[Peng], Zhao, C.[Cheng], Zhang, Y.T.[Yong-Tao], Zhang, S.C.[Shao-Chong], Zhao, X.Y.[Xin-Yu], Lu, L.[Lin], Zhang, G.M.[Guo-Ming], Lei, B.[Baiying],
FIT-Net: Feature Interaction Transformer Network for Pathologic Myopia Diagnosis,
MedImg(42), No. 9, September 2023, pp. 2524-2538.
IEEE DOI 2310
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Bi, Q.[Qi], Sun, X.[Xu], Yu, S.[Shuang], Ma, K.[Kai], Bian, C.[Cheng], Ning, M.[Munan], He, N.[Nanjun], Huang, Y.W.[Ya-Wen], Li, Y.X.[Yue-Xiang], Liu, H.[Hanruo], Zheng, Y.F.[Ye-Feng],
MIL-ViT: A multiple instance vision transformer for fundus image classification,
JVCIR(97), 2023, pp. 103956.
Elsevier DOI Code:
WWW Link. 2312
Vision transformer, Multiple instance learning, Fundus image, Attention aggregation, Calibrated attention mechanism BibRef

Ju, L.[Lie], Yu, Z.[Zhen], Wang, L.[Lin], Zhao, X.[Xin], Wang, X.[Xin], Bonnington, P.[Paul], Ge, Z.Y.[Zong-Yuan],
Hierarchical Knowledge Guided Learning for Real-World Retinal Disease Recognition,
MedImg(43), No. 1, January 2024, pp. 335-350.
IEEE DOI 2401
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Tang, Z.[Zhiri], Wong, H.S.[Hau-San], Yu, Z.[Zekuan],
Ocular Disease Recognition via Differential Privacy and Unsupervised Domain Regularizer,
SPLetters(31), 2024, pp. 136-140.
IEEE DOI 2401
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Zhang, W.[Wentian], Liu, H.Z.[Hao-Zhe], Xie, J.[Jinheng], Huang, Y.W.[Ya-Wen], Zhang, Y.[Yu], Li, Y.X.[Yue-Xiang], Ramachandra, R.[Raghavendra], Zheng, Y.F.[Ye-Feng],
Anomaly detection via gating highway connection for retinal fundus images,
PR(148), 2024, pp. 110167.
Elsevier DOI Code:
WWW Link. 2402
Anomaly detection, Feature prediction, Fundus image, Skip connection BibRef


Akinniyi, O.[Oluwatunmise], Razzak, I.[Imran], Rahman, M.M.[Md Mahmudur], Sandhu, H.[Harpal], El-Baz, A.[Ayman], Khalifa, F.[Fahmi],
Multi-Classification of Retinal Diseases Using a Pyramidal Ensemble Deep Framework,
ICIP23(1945-1949)
IEEE DOI 2312
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Zhang, J.[Junkang], Wen, B.[Bo], Kalaw, F.G.P.[Fritz Gerald P.], Cavichini, M.[Melina], Bartsch, D.U.G.[Dirk-Uwe G.], Freeman, W.R.[William R.], Nguyen, T.Q.[Truong Q.], An, C.[Cheolhong],
Accurate Registration between Ultra-Wide-Field and Narrow Angle Retina Images with 3D Eyeball Shape Optimization,
ICIP23(2750-2754)
IEEE DOI 2312
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Wu, T.W.[Ting-Wei], Huang, J.H.[Jia-Hong], Lin, J.[Joseph], Worring, M.[Marcel],
Expert-defined Keywords Improve Interpretability of Retinal Image Captioning,
WACV23(1859-1868)
IEEE DOI 2302
Heating systems, Measurement, Medical services, Predictive models, Retina, Biomedical imaging, Applications: Biomedical/healthcare/medicine BibRef

Avramidis, K.[Kleanthis], Rostami, M.[Mohammad], Chang, M.[Melinda], Narayanan, S.[Shrikanth],
Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning,
ICIP22(3973-3977)
IEEE DOI 2211
Integrated optics, Deep learning, Training, Location awareness, Optical imaging, Feature extraction, Robustness, human-centered AI, multi-view learning BibRef

Jeon, M.[Minkyu], Park, H.[Hyeonjin], Kim, H.W.J.[Hyun-Woo J.], Morley, M.[Michael], Cho, H.[Hyunghoon],
k-SALSA: k-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment,
ECCV22(XXI:661-678).
Springer DOI 2211
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Li, M.J.[Ming-Jie], Cai, W.J.[Wen-Jia], Verspoor, K.[Karin], Pan, S.R.[Shi-Rui], Liang, X.D.[Xiao-Dan], Chang, X.J.[Xiao-Jun],
Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation,
CVPR22(20624-20633)
IEEE DOI 2210
Training, Visualization, Unified modeling language, Benchmark testing, Writing, Transformers, Image restoration, Medical, Vision + language BibRef

Sangiovanni, M.[Mara], Frucci, M.[Maria], Riccio, D.[Daniel], di Perna, L.[Luigi], Simonelli, F.[Francesca], Brancati, N.[Nadia],
Exploring a Transformer Approach for Pigment Signs Segmentation in Fundus Images,
MEDXF22(329-339).
Springer DOI 2208
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Haraburda, P.[Patrycja], Dabala, L.[Lukasz],
Eye Diseases Classification Using Deep Learning,
CIAP22(I:160-172).
Springer DOI 2205
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Huang, J.H.[Jia-Hong], Wu, T.W.[Ting-Wei], Yang, C.H.H.[C.H. Huck], Shi, Z.L.[Zeng-Lin], Lin, I.H.[I-Hung], Tegner, J.[Jesper], Worring, M.[Marcel],
Non-local Attention Improves Description Generation for Retinal Images,
WACV22(3250-3259)
IEEE DOI 2202
Measurement, Computational modeling, Retina, Task analysis, Medical diagnostic imaging, Mutual information, Vision Systems and Applications BibRef

Huang, J.H.[Jia-Hong], Wu, T.W.[Ting-Wei], Yang, C.H.H.[Chao-Han Huck], Worring, M.[Marcel],
Deep Context-Encoding Network for Retinal Image Captioning,
ICIP21(3762-3766)
IEEE DOI 2201
Measurement, Image coding, Retina, Decoding, Task analysis, Biomedical imaging, Image Captioning, Medical Report Generation, and Context-Encoding BibRef

Zhao, S.[Shu], Chen, W.Y.[Wei-Yang],
Retinal Image Segmentation Based on Multiple Features Method,
ICIVC21(124-128)
IEEE DOI 2112
Integrated optics, Image segmentation, Biomedical optical imaging, Veins, Blood vessels, Retina, standard deviation BibRef

Kamran, S.A.[Sharif Amit], Hossain, K.F.[Khondker Fariha], Tavakkoli, A.[Alireza], Zuckerbrod, S.L.[Stewart Lee], Baker, S.A.[Salah A.],
VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers,
CVAMD21(3228-3238)
IEEE DOI 2112
Integrated optics, In vivo, Computer architecture, Retina, Generative adversarial networks, Transformers, Optical imaging BibRef

Forero, M.G.[Manuel G.], Beltrán, C.E.[Carlos E.], Troncoso, A.[Armando], González-Santos, C.[Christian],
Classification of Cattleya Trianae and Its Varieties by Using Colorimetry,
MCPR20(35-44).
Springer DOI 2007
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Chelaramani, S.[Sahil], Gupta, M.[Manish], Agarwal, V.[Vipul], Gupta, P.[Prashant], Habash, R.[Ranya],
Multi-Task Knowledge Distillation for Eye Disease Prediction,
WACV21(3982-3992)
IEEE DOI 2106
Measurement, Retinopathy, Training data, Melanoma, Predictive models, Retina, Data models BibRef

Huang, J.H.[Jia-Hong], Yang, C.H.H.[C.H. Huck], Liu, F.Y.[Fang-Yu], Tian, M.[Meng], Liu, Y.C.[Yi-Chieh], Wu, T.W.[Ting-Wei], Lin, I.H.[I-Hung], Wang, K.[Kang], Morikawa, H.[Hiromasa], Chang, H.[Hernghua], Tegner, J.[Jesper], Worring, M.[Marcel],
DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation,
WACV21(2441-2451)
IEEE DOI 2106
Visualization, Retina, Generators, Medical diagnostic imaging, Diseases BibRef

Ferrari, C.[Claudio], Berlincioni, L.[Lorenzo], Bertini, M.[Marco], del Bimbo, A.[Alberto],
Inner Eye Canthus Localization for Human Body Temperature Screening,
ICPR21(8833-8840)
IEEE DOI 2105
Temperature measurement, Solid modeling, Privacy, Annotations, Manuals, Cameras BibRef

Zhao, R.[Ruohan], Li, Q.[Qin], You, J.[Jane],
Robust Localization of Retinal Lesions via Weakly-supervised Learning,
ICPR21(4613-4618)
IEEE DOI 2105
Location awareness, Training, Pathology, Solid modeling, Annotations, Semantics, Retina BibRef

Wei, Q.J.[Qi-Jie], Li, X.R.[Xi-Rong], Yu, W.H.[Wei-Hong], Zhang, X.[Xiao], Zhang, Y.P.[Yong-Peng], Hu, B.[Bojie], Mo, B.[Bin], Gong, D.[Di], Chen, N.[Ning], Ding, D.[Dayong], Chen, Y.X.[You-Xin],
Learn to Segment Retinal Lesions and Beyond,
ICPR21(7403-7410)
IEEE DOI 2105
Image segmentation, Art, Retinopathy, Semantics, Training data, Retina, Pattern recognition BibRef

Le, N.[Ngan], Le, T.[Trung], Yamazaki, K.[Kashu], Bui, T.[Toan], Luu, K.[Khoa], Savides, M.[Marios],
Offset Curves Loss for Imbalanced Problem in Medical Segmentation,
ICPR21(9189-9195)
IEEE DOI 2105
Image segmentation, Fitting, Semantics, Object segmentation, Retina, Brain modeling BibRef

Fang, X.[Xi], Yang, J.C.[Jian-Cheng], Ni, B.B.[Bing-Bing],
Stochastic Label Refinery: Toward Better Target Label Distribution,
ICPR21(9115-9121)
IEEE DOI 2105
Training, Retinopathy, Supervised learning, Neural networks, Refining, Stochastic processes, Training data BibRef

Ilyasova, N.[Nataly], Shirokanev, A.[Alexandr], Demin, N.[Nikita], Zolotarev, A.[Andrey],
High-performance Algorithms Application for Retinal Image Segmentation Based on Texture Features,
IMTA20(198-210).
Springer DOI 2103
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Zhou, K.[Kang], Xiao, Y.T.[Yu-Ting], Yang, J.L.[Jian-Long], Cheng, J.[Jun], Liu, W.[Wen], Luo, W.X.[Wei-Xin], Gu, Z.W.[Zai-Wang], Liu, J.[Jiang], Gao, S.H.[Sheng-Hua],
Encoding Structure-texture Relation with P-net for Anomaly Detection in Retinal Images,
ECCV20(XX:360-377).
Springer DOI 2011
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Meng, Q., Shin'ichi, S.,
ADINet: Attribute Driven Incremental Network for Retinal Image Classification,
CVPR20(4032-4041)
IEEE DOI 2008
Diseases, Retina, Training, Entropy, Predictive models, Knowledge engineering, Biomedical imaging BibRef

Peskine, Y.[Youri], Boucher, M.C.[Marie-Carole], Cheriet, F.[Farida],
An Interpretable Data-driven Score for the Assessment of Fundus Images Quality,
ICIAR20(II:325-331).
Springer DOI 2007
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ElMahmoudy, S.[Safinaz], Abdel-Hamid, L.[Lamiaa], El-Rafei, A.[Ahmed], El-Ramly, S.[Salwa],
Wavelet-based Retinal Image Enhancement,
ICIAR20(II:313-324).
Springer DOI 2007
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Gaudio, A.[Alex], Smailagic, A.[Asim], Campilho, A.[Aurélio],
Enhancement of Retinal Fundus Images via Pixel Color Amplification,
ICIAR20(II:299-312).
Springer DOI 2007
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Truong, P., Apostolopoulos, S., Mosinska, A., Stucky, S., Ciller, C., Zanet, S.D.,
GLAMpoints: Greedily Learned Accurate Match Points,
ICCV19(10731-10740)
IEEE DOI 2004
convolutional neural nets, diseases, eye, feature extraction, greedy algorithms, image matching, image registration, diagnostic imaging BibRef

Kaplan, S.[Sinan], Lensu, L.[Lasse], Laaksonen, L.[Lauri], Uusitalo, H.[Hannu],
Evaluation of Unconditioned Deep Generative Synthesis of Retinal Images,
ACIVS20(262-273).
Springer DOI 2003
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Lai, X.[Xin], Li, X.R.[Xi-Rong], Qian, R.[Rui], Ding, D.[Dayong], Wu, J.[Jun], Xu, J.P.[Jie-Ping],
Four Models for Automatic Recognition of Left and Right Eye in Fundus Images,
MMMod19(I:507-517).
Springer DOI 1901
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Liu, Y.[Yun], Ren, G.[Gang], Yang, G.P.[Gong-Ping], Xi, X.M.[Xiao-Ming], Chen, X.J.[Xin-Jian], Yin, Y.L.[Yi-Long],
Fully Convolutional Network and Graph-Based Method for Co-Segmentation of Retinal Layer on Macular OCT Images,
ICPR18(3081-3085)
IEEE DOI 1812
Retina, Image segmentation, Image edge detection, Computer architecture, Computational modeling, Task analysis, retinal layer segmentation BibRef

Zhang, H., Liu, X., Wang, G., Chen, Y., Zhao, W.,
An Automated Point Set Registration Framework for Multimodal Retinal Image,
ICPR18(2857-2862)
IEEE DOI 1812
Feature extraction, Retina, Image registration, Task analysis, Estimation, Imaging, Measurement, multimodal retinal image, adaptive mismatches removing BibRef

da Silva, I.F.S.[Italo Francyles Santos], de Almeida, J.D.S.[Joăo Dallyson Sousa], Teixeira, J.A.M.[Jorge Antonio Meireles], Junior, G.B.[Geraldo Braz], de Paiva, A.C.[Anselmo Cardoso],
Segmentation of the Retinal Reflex in Brückner Test Images Using U-Net Convolutional Network,
ICIAR18(679-686).
Springer DOI 1807
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Razgulin, A.V., Iroshnikov, N.G., Larichev, A.V., Romanenko, T.E., Goncharov, A.S.,
Fourier Domain Iterative Approach to Optical Sectioning of 3d Translucent Objects for Ophthalmology Purposes,
PTVSBB17(173-177).
DOI Link 1805
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Nasonov, A., Chesnakov, K., Krylov, A.,
CNN Based Retinal Image Upscaling Using Zero Component Analysis,
PTVSBB17(27-31).
DOI Link 1805
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Colomer, A., Naranjo, V., Angulo, J.,
Colour normalization of fundus images based on geometric transformations applied to their chromatic histogram,
ICIP17(3135-3139)
IEEE DOI 1803
biomedical optical imaging, diseases, eye, image colour analysis, image segmentation, image texture, medical image processing, fundus images BibRef

Samagaio, G.[Gabriela], de Moura, J.[Joaquim], Novo, J.[Jorge], Ortega, M.[Marcos],
Optical Coherence Tomography Denoising by Means of a Fourier Butterworth Filter-Based Approach,
CIAP17(II:422-432).
Springer DOI 1711
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Katona, M.[Melinda], Kovács, A.[Attila], Dégi, R.[Rózsa], Nyúl, L.G.[László G.],
Automatic Detection of Subretinal Fluid and Cyst in Retinal Images,
CIAP17(I:606-616).
Springer DOI 1711
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Caramihale, T.[Traian], Popescu, D.[Dan], Ichim, L.[Loretta],
Interconnected Neural Networks Based on Voting Scheme and Local Detectors for Retinal Image Analysis and Diagnosis,
CIAP17(II:753-764).
Springer DOI 1711
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Ai, W.W.[Wen-Wen], Shao, F.J.[Feng-Jing], Sun, R.C.[Ren-Cheng],
Discovering the pathological mechanism based on the locus interaction networks with differential analysis,
ICIVC17(288-294)
IEEE DOI 1708
Bioinformatics, Databases, Diseases, Genomics, Pathology, Switches, differential network, locus genotypes interaction networks, retinitis Pigmentosa BibRef

Lazareva, A.[Anfisa], Asad, M.[Muhammad], Slabaugh, G.[Greg],
Learning to Deblur Adaptive Optics Retinal Images,
ICIAR17(497-506).
Springer DOI 1706
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Ilyasova, N.[Nataly], Paringer, R.[Rustam], Kupriyanov, A.[Alexander],
Regions of Interest in a Fundus Image Selection Technique Using the Discriminative Analysis Methods,
ICCVG16(408-417).
Springer DOI 1611
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Roy, P.K.[Pallab K.], Chakravorty, R., Sedai, S.[Suman], Mahapatra, D.[Dwarikanath], Garnavi, R.[Rahil],
Automatic Eye Type Detection in Retinal Fundus Image Using Fusion of Transfer Learning and Anatomical Features,
DICTA16(1-7)
IEEE DOI 1701
Adaptive optics BibRef

Mahapatra, D.[Dwarikanath], Roy, P.K.[Pallab K.], Sedai, S.[Suman], Garnavi, R.[Rahil],
Retinal Image Quality Classification Using Saliency Maps and CNNs,
MLMI16(172-179).
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Mokhtari, M., Rabbani, H., Mehri-Dehnavi, A.,
Alignment of optic nerve head optical coherence tomography B-scans in right and left eyes,
ICIP17(2279-2283)
IEEE DOI 1803
BibRef
Earlier: ICIP16(2355-2359)
IEEE DOI 1610
biomedical optical imaging, blood vessels, diseases, eye, feature extraction, medical image processing, optical tomography, alignment BibRef

Joshi, V., Agurto, C., Barriga, E., Nemeth, S., Soliz, P.,
Clinical utilization of automated image analysis software for improving retinal reader's performance,
Southwest16(145-148)
IEEE DOI 1605
Graphical user interfaces BibRef

Hassan, H.A., Tahir, N.M., Yassin, I., Yahaya, C.H.C., Shafie, S.M.,
Visualisation of exudates in fundus images using radar chart and color auto correlogram technique,
ICCVIA15(1-6)
IEEE DOI 1603
diseases BibRef

Mahmudi, T.[Tahereh], Kafieh, R.[Rahele], Rabbani, H.[Hossein], Mehri, A.[Alireza], Akhlagi, M.[Mohammadreza],
Asymmetry evaluation of fundus images in right and left eyes using radon transform and fractal analysis,
ICIP15(163-167)
IEEE DOI 1512
Asymmetry Analysis; Fractal Dimension; Fundus images; Radon Transform BibRef

Ong, E.P.[Ee Ping], Xu, Y.[Yanwu], Wong, D.W.K.[Damon Wing Kee], Liu, J.[Jiang],
Retina verification using a combined points and edges approach,
ICIP15(2720-2724)
IEEE DOI 1512
Least-Median-Squares estimator BibRef

Kannan, R., Ghinea, G., Kannaiyan, S., Swaminathan, S.,
CLRF: Compressed Local Retinal Features for image description,
ICAPR15(1-5)
IEEE DOI 1511
data compression BibRef

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Performance analysis of unconventional dictionary on retinal images,
ICIP14(2275-2279)
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ICCV15(1832-1840)
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Complexity theory BibRef

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NCVPRIPG13(1-4)
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ICIP13(1423-1427)
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ICPR92(I:509-513).
IEEE DOI BibRef 9200

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Eye, Cornea, Corneal Images .


Last update:Mar 16, 2024 at 20:36:19