21.4.8 Surveys, Comparisons, Cells, DNA

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
BibRef

Raj, A.[Aditya], Tiwari, A.K.[Anil Kumar], Martini, M.G.[Maria G.],
Fundus image quality assessment: survey, challenges, and future scope,
IET-IPR(13), No. 8, 20 June 2019, pp. 1211-1224.
DOI Link 1906
BibRef

Sukhia, K.N.[Komal Nain], Riaz, M.M.[Muhammad Mohsin], Ghafoor, A.[Abdul],
Content-based retinal image retrieval,
IET-IPR(13), No. 9, 18 July 2019, pp. 1525-1534.
DOI Link 1907
BibRef

Gong, C., Erichson, N.B., Kelly, J.P., Trutoiu, L., Schowengerdt, B.T., Brunton, S.L., Seibel, E.J.,
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology,
MedImg(38), No. 8, August 2019, pp. 1993-2004.
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,
CIAP19(II:437-445).
Springer DOI 1909
BibRef

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,
PR(98), 2020, pp. 107068.
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)
DOI Link 1806
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,
IET-IPR(13), No. 14, 12 December 2019, pp. 2778-2789.
DOI Link 1912
BibRef

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.
DOI Link 1912
Adaptive optics, Eye models, Eye movements, Image metrics, Optical coherence tomography, Power spectral density BibRef

Zhang, F.[Fang], Xu, X.[Xu], Xiao, Z.[Zhitao], Wu, J.[Jun], Geng, L.[Lei], Wang, W.[Wen], Liu, Y.B.[Yan-Bei],
Automated quality classification of colour fundus images based on a modified residual dense block network,
SIViP(14), No. 1, February 2020, pp. 215-223.
Springer DOI 2001
BibRef

Cherukuri, V., BG, V.K.[V. Kumar], Bala, R., Monga, V.,
Deep Retinal Image Segmentation With Regularization Under Geometric Priors,
IP(29), 2020, pp. 2552-2567.
IEEE DOI 2001
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.
DOI Link 2005
feature fusion, intensity hue saturation, support vector machine, wavelet packet transform, weighted principal component analysis BibRef

Ramli, R.[Roziana], Idris, M.Y.I.[Mohd Yamani Idna], Hasikin, K.[Khairunnisa], Karim, N.K.A.[Noor Khairiah A.], Wahab, A.W.A.[Ainuddin Wahid Abdul], Ahmedy, I.[Ismail], Ahmedy, F.[Fatimah], Arof, H.[Hamzah],
Local descriptor for retinal fundus image registration,
IET-CV(14), No. 4, June 2020, pp. 144-153.
DOI Link 2005
BibRef

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.
DOI Link 2105
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.
DOI Link 2106
BibRef

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.
DOI Link 2106
BibRef

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.
DOI Link 2108
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

Zhang, W.T.[Wen-Tian], Liu, H.Z.[Hao-Zhe], Xie, J.H.[Jin-Heng], 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

Shaik, N.S.[Nagur Shareef], Cherukuri, T.K.[Teja Krishna],
Gated contextual transformer network for multi-modal retinal image clinical description generation,
IVC(143), 2024, pp. 104946.
Elsevier DOI 2403
Clinical description generation, Expert-defined clinical keywords, Gated contextual attention, Visual explanation BibRef

Sau, P.C.[Paresh Chandra], Gupta, M.[Manish], Bansal, A.[Atul],
Optimized ResUNet++-Enabled Blood Vessel Segmentation for Retinal Fundus Image Based on Hybrid Meta-Heuristic Improvement,
IJIG(24), No. 3, May 2024, pp. 2450033.
DOI Link 2406
BibRef

Xia, X.[Xue], Li, Y.[Ying], Xiao, G.[Guobei], Zhan, K.[Kun], Yan, J.H.[Jin-Hua], Cai, C.[Chao], Fang, Y.M.[Yu-Ming], Huang, G.[Guofu],
Benchmarking deep models on retinal fundus disease diagnosis and a large-scale dataset,
SP:IC(127), 2024, pp. 117151.
Elsevier DOI Code:
WWW Link. 2408
Dataset, Benchmark, Ocular disease diagnosis, Medical image analysis, Attention network BibRef

Gadde, S.S.[Sai Sudha], Kiran, K.V.D.,
Entropy-Based Feature Extraction Model for Fundus Images with Deep Learning Model,
IJIG(24), No. 5, September 2024, pp. 2340006.
DOI Link 2410
BibRef

Huang, K.[Kun], Ma, X.[Xiao], Zhang, Z.[Zetian], Zhang, Y.H.[Yu-Han], Yuan, S.T.[Song-Tao], Fu, H.Z.[Hua-Zhu], Chen, Q.[Qiang],
Diverse Data Generation for Retinal Layer Segmentation With Potential Structure Modeling,
MedImg(43), No. 10, October 2024, pp. 3584-3595.
IEEE DOI Code:
WWW Link. 2411
Image segmentation, Retina, Data models, Diseases, Pathology, Training, Task analysis, Optical coherence tomography, layer segmentation, contrast learning BibRef

Rong, Y.B.[Yi-Biao], Lin, T.[Tian], Chen, H.Y.[Hao-Yu], Fan, Z.[Zhun], Chen, X.J.[Xin-Jian],
Searching Discriminative Regions for Convolutional Neural Networks in Fundus Image Classification With Genetic Algorithms,
IP(33), 2024, pp. 5949-5958.
IEEE DOI 2411
Image classification, Visualization, Glaucoma, Genetic algorithms, Deep learning, Convolutional neural networks, Search problems, fundus image classification BibRef

Zhang, X.F.[Xin-Feng], Zhang, J.[JiaMing], Shao, J.[Jie], Li, H.[Hui], Liu, X.M.[Xiao-Min], Jia, M.[Maoshen],
A semi-supervised segmentation network fusing pseudo-label with multi-level feature consistency correction for hard exudates,
IET-IPR(18), No. 13, 2024, pp. 4411-4421.
DOI Link 2411
image segmentation, medical image processing BibRef

Shaik, N.S.[Nagur Shareef], Cherukuri, T.K.[Teja Krishna], Ye, D.H.[Dong Hye],
M3T: Multi-Modal Medical Transformer To Bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generation,
ICIP24(3037-3043)
IEEE DOI 2411
Visualization, Visual impairment, Semantics, Streaming media, Retina, Transformers, Planning, Medical Description Generation BibRef

Go, S.[Sojung], Ji, Y.[Younghoon], Park, S.J.[Sang Jun], Lee, S.[Soochahn],
Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models,
EnhanceMedIm24(2335-2344)
IEEE DOI 2410
Image segmentation, Data privacy, Accuracy, Retina, Diffusion models, Retinal Fundus Image, Diffusion Model, Artery/Vein Mask, Image Generation BibRef

Bao, J.[Jinan], Sun, H.[Hanshi], Deng, H.Q.[Han-Qiu], He, Y.S.[Yin-Sheng], Zhang, Z.X.[Zhao-Xiang], Li, X.Y.[Xing-Yu],
BMAD: Benchmarks for Medical Anomaly Detection,
VAND24(4042-4053)
IEEE DOI Code:
WWW Link. 2410
Benchmark testing, Video surveillance, Retina, Medical diagnosis, Medical diagnostic imaging, Anomaly detection, benchmark BibRef

Li, L.[Lingrui], Zhou, Y.F.[Yan-Feng], Yang, G.[Ge],
Robust Source-Free Domain Adaptation for Fundus Image Segmentation,
WACV24(7825-7834)
IEEE DOI Code:
WWW Link. 2404
Training, Image segmentation, Adaptation models, Costs, Computational modeling, Source coding, Applications, Biomedical / healthcare / medicine 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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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).
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Haraburda, P.[Patrycja], Dabala, L.[Lukasz],
Eye Diseases Classification Using Deep Learning,
CIAP22(I:160-172).
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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).
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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 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).
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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

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An Interpretable Data-driven Score for the Assessment of Fundus Images Quality,
ICIAR20(II:325-331).
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Wavelet-based Retinal Image Enhancement,
ICIAR20(II:313-324).
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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).
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MMMod19(I:507-517).
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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.[João Dallyson Sousa], Teixeira, J.A.M.[Jorge Antonio Meireles], Junior, G.B.[Geraldo Braz], de Paiva, A.C.[Anselmo Cardoso],
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Razgulin, A.V., Iroshnikov, N.G., Larichev, A.V., Romanenko, T.E., Goncharov, A.S.,
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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,
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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).
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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

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Learning to Deblur Adaptive Optics Retinal Images,
ICIAR17(497-506).
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Regions of Interest in a Fundus Image Selection Technique Using the Discriminative Analysis Methods,
ICCVG16(408-417).
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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,
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Alignment of optic nerve head optical coherence tomography B-scans in right and left eyes,
ICIP17(2279-2283)
IEEE DOI 1803
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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)
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data compression BibRef

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IEEE DOI 0810
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ICIP07(III: 309-312).
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IEEE DOI 0612
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Retinal Spot Lesion Detection Using Adaptive Multiscale Morphological Processing,
ISVC06(II: 490-501).
Springer DOI 0611
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Yang, X.Y.[Xiao-Yun], Morrow, P.J., Scotney, B.W.,
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ICIP06(65-68).
IEEE DOI 0610
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Tang, Y.D.[Yan-Dong], Li, X.M.[Xiao-Mao], von Freyberg, A.[Axel], Goch, G.[Gert],
Automatic Segmentation of the Papilla in a Fundus Image Based on the C-V Model and a Shape Restraint,
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IEEE DOI 0609
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IEEE DOI 0505
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Techniques for temporal registration of retinal images,
ICIP04(II: 1089-1092).
IEEE DOI 0505
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Earlier:
Reconstruction of vascular structures in retinal images,
ICIP03(II: 157-160).
IEEE DOI 0312
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Li, H.Q.[Hui-Qi], Chutatape, O.,
A model-based approach for automated feature extraction in fundus images,
ICCV03(394-399).
IEEE DOI 0311
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Chanwimaluang, T., Fan, G.L.[Guo-Liang],
An efficient algorithm for extraction of anatomical structures in retinal images,
ICIP03(I: 1093-1096).
IEEE DOI 0312
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Li, H.Q.[Hui-Qi], Hsu, W.[Wynne], Lee, M.L.[Mong Li], Wang, H.Y.[Hong-Yu],
A piecewise Gaussian model for profiling and differentiating retinal vessels,
ICIP03(I: 1069-1072).
IEEE DOI 0312
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Li, H., Chutatape, O.,
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ICIP01(II: 837-840).
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Wang, H.[Huan], Hsu, W.[Wynne], Goh, K.G.[Kheng Guan], Lee, M.L.[Mong Li],
An Effective Approach to Detect Lesions in Color Retinal Images,
CVPR00(II: 181-186).
IEEE DOI 0005
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Guest, R.M., Fairhurst, M.C., Potter, J.M., Donnelly, N.,
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ICPR00(Vol IV: 316-319).
IEEE DOI 0009
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3d Fundus Pattern Reconstruction and Display from Multiple Fundus Images,
ICPR00(Vol IV: 94-97).
IEEE DOI 0009
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Detection of Abnormalities in Retinal Images using Digital Image Analysis,
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Texture Analysis of Retinal Images to Determine Nerve Fibre Loss,
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Automated Diagnosis and Image Understanding with Object Extraction, Object Classification, and Inferencing in Retinal Images,
ICIP96(III: 695-698).
IEEE DOI BibRef 9600

Hart, W.E., Goldbaum, M.H.,
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ICIP94(III: 576-580).
IEEE DOI 9411
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Harbarth, U.P., Reiter, K., Zyzyck, J., Zhou, Q.Y.[Qien-Yuan], Morris, B., Dreher, A.W.,
Confocal laser scanning and advanced image processing in ophthalmology,
ICIP96(III: 683-686).
IEEE DOI 9610
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Petrig, B.[Benno], Bigün, J.[Josef], Curchod, M.G.[Marc G.],
Motion Estimation of Ocular Fundus Images,
ICIP96(III: 691-694).
IEEE DOI BibRef 9600

Meseure, P., Rouland, J.F., Dubois, P., Karpf, S., Chaillou, C.,
Sophocle: A Retinal Laser Photocoagulation Simulator: Overview,
CVRMed95(XX-YY) BibRef 9500

Barrett, S.F., Rylander, III, H.G.[H. Grady], Welch, A.J.,
Automated lesion data base building for the treatment of retinal disorders,
ICIP94(I: 426-430).
IEEE DOI 9411
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Okutomi, M., Yoshzaki, O., Tomita, G.,
Color Stereo Matching and Its Application to 3-D Measurement of Optic Nerve Head,
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:Nov 26, 2024 at 16:40:19