Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

Sedai, Suman; Roy, Pranab; Mahapatra, Dwarikanath; Garnavi, Rahil

doi:10.17077/omia.1040

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…He described a method based on a fuzzy convergence voting mechanism to find the OD location (14). S.Sedai et al introduce an automatic regression based method in order to segmentation of optic cup and optic disc in retinal color fundus images (15). Handayani et al detected OD by applying Hough Transform and Active Contour Models.…”

Section: Review Of the Related Literature Optic Disk Localization Andmentioning

confidence: 99%

The computer based method to diabetic retinopathy assessment in retinal images: a review

et al. 2019

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Diabetic retinopathy accounts for a considerable amount of the blindness especially among the patients who are between 20 to 60 years. The early detection of this disease plays an important role in preventing vision damages and appropriate follow-up care of diabetic eye. Manual investigation of color fundus images to check morphological changes in dark and bright lesions is tedious work and very time-consuming that can be made easily with the help of computer-aided diagnosis system. Many techniques were proposed for early detection of the abnormalities in the retinal images help the ophthalmologists recognize the retinopathy earlier. This paper presents a review of various automated algorithms that have been used for the detection of diabetic retinopathy.

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Section: Review Of the Related Literature Optic Disk Localization Andmentioning

confidence: 99%

The computer based method to diabetic retinopathy assessment in retinal images: a review

et al. 2019

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“…For this comparison the F -score, which is defined as F = 2P × R/ P + R was calculated, where P is precision and R is the recall. For instance, Sedai et al [31] obtained an F -score of 0 86 using only 50 of the 101 images in the Drishti-GS1 database and Chakravarty et al [32] obtained an F -score of 0.81 using the whole Drishti-GS1 database. As in the latter, in this work, all the images in the Drishti-GS1 were used; obtaining an F -score of 0.77.…”

Section: Cup Segmentationmentioning

confidence: 99%

Computer-Aided Glaucoma Diagnosis Using Stochastic Watershed Transformation on Single Fundus Images

Diaz-Pinto¹,

Morales²,

Naranjo³

et al. 2019

j med imaging hlth inform

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Glaucoma is a chronic eye disease and one of the major causes of permanent blindness. Since it does not show initial symptoms, early diagnosis is important to limit its progression. This paper presents an automatic optic nerve characterization algorithm for glaucoma diagnosis based only on retinal fundus images. For optic cup segmentation, we used a new method based on the stochastic watershed transformation applied on the YIQ colour space to extract clinical indicators such as the Cup/Disc ratio, the area Cup/Disc ratio and the ISNT rule. Afterwards, an assessment between normal and glaucomatous fundus images is performed. The proposed algorithm was evaluated on 6 different (private and public) databases containing 723 images (377 normal and 346 glaucomatous images) which achieved a specificity and sensitivity of 0.674 and 0.675, respectively. Moreover, an F-score of 0.770 was obtained when evaluating this method on the publicly available database Drishti-GS1. A comparison of the proposed work with other state-of-the-art methods demonstrates the robustness of the proposed algorithm; because it was tested using images from different databases with high variability, which is a common issue in this area. Additional comparisons with existing works for cup segmentation, that use the publicly available database Drishti-GS1, are also presented in this paper.

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“…[34,35,36,37,38,39] use CNNs and reinforcement learning for iterative registration of CT to cone-beam CT in cardiac and abdominal images. FlowNet [40,41,42,43,44,45] formulates dense optical flow estimation as a regression task using CNNs and uses it for image matching. These approaches still use a conventional model to generate the transformed image from the deformation field which increases computation time and does not fully utilize the generative capabilities of DL methods for the purpose of generating registered images.…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration

Mahapatra¹

2019

Preprint

Self Cite

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Medical image registration is an important task in automated analysis of multimodal images and temporal data involving multiple patient visits. Conventional approaches, although useful for different image types, are time consuming. Of late, deep learning (DL) based image registration methods have been proposed that outperform traditional methods in terms of accuracy and time. However, DL based methods are heavily dependent on training data and do not generalize well when presented with images of different scanners or anatomies. We present a DL based approach that can perform medical image registration of one image type despite being trained with images of a different type. This is achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive retraining.To achieve our objective we train a network that transforms the given input image pair to a latent feature space vector using autoencoders. The resultant encoded feature space is used to generate the registered images with the help of generative adversarial networks (GANs). This feature transformation ensures greater invariance to the input image type. Experiments on chest Xray, retinal and brain MR images show that our method, trained on one dataset gives better registration performance for other datasets, outperforming conventional methods that do not incorporate domain adaptation.

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Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

Cited by 12 publications

References 13 publications

The computer based method to diabetic retinopathy assessment in retinal images: a review

The computer based method to diabetic retinopathy assessment in retinal images: a review

Computer-Aided Glaucoma Diagnosis Using Stochastic Watershed Transformation on Single Fundus Images

Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration

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