Retina image–based optic disc segmentation

Wang, Ching-Lin; Hsieh, Ming-Yuan; Hung, Yi-Wen; Tsai, Meng‐Hsiun; Chan, Mao-Hsiang; Chen, Jui-Ming; Tung, Kwong‐Chung

doi:10.1177/1687814016649298

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…In the past, researchers have developed many algorithms for automatic MA segmentation. Most of the ML methods mainly locate the position of the MA according to the fundus features, 10,11 and then extract the features of the MA to segment it, such as Super‐pixel Classification, 12 Hough transform 13 and Otsu threshold method 14 . However, these methods are sensitive to image brightness, texture, and fundus morphology.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the ML methods mainly locate the position of the MA according to the fundus features, 10,11 and then extract the features of the MA to segment it, such as Super-pixel Classification, 12 Hough transform 13 and Otsu threshold method. 14 However, these methods are sensitive to image brightness, texture, and fundus morphology. During the segmentation process, the high contrast of retinal blood vessels or the deformed shape caused by lesions will adversely affect the results.…”

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confidence: 99%

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Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

Jayachandran,

Ratheesh Kumar

2023

Int J Imaging Syst Tech

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Microaneurysms (MAs) are the early indications of diabetic retinopathy (DR), which may result in total visual loss. MAs detection is an exciting work due to its small, darkish color and subtle nature. The automatic detection and categorization of MAs in retinal fundus images using a multi‐scale approach based on Deep Neural Networks and Neighborhood Analysis is proposed in this research article. MAs segmentation, classification, and preprocessing comprise the three steps of the proposed technique. To extract the multi‐scale MA features, the auto‐weight dilated convolutional unit (AD) is specifically used for convolutional feature maps. To fuse convolutional feature maps in encoding layers, the AD unit used a learnable set of parameters. An efficient architecture for feature extraction during the encoding step is incorporated into the AD unit. We integrated the AD unit with the benefits of the U‐Net network for deep and shallow features. Additionally, in order to improve the suggested model and produce the final derivation, we developed a novel optimization approach. After that, neighborhood analysis is performed to name the Micro‐aneurysm because the lesion is actually a collection of independent little images rather than the entire image. The classification accuracy of the proposed method for the three different data sets such as MESSIDOR, online retinal fundus image database for glaucoma analysis (ORIGA), and RIM‐ONE‐R1, is 99.28%, 98.95%, and 98.76% respectively. The results show a good performance of the proposed model against the other analyzed procedure.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

Jayachandran,

Ratheesh Kumar

2023

Int J Imaging Syst Tech

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“…For example, the cup-to-disc ratio (CDR), which measures the ratio of the disc to its central depression (cup), is widely used to diagnose glaucoma [22,20,23]. Glaucomatous eyes show more pronounced cupping due to an increase in intracranial pressure, which causes flowing nutrients to pass through damaged axons and induce swelling of the optic disc [21]. Optic disc swelling can also be a sign of a brain tumor or a minor nerve stroke.…”

Section: Introductionmentioning

confidence: 99%

Optic Disc Segmentation using Disk-Centered Patch Augmentation

Motevali,

Khanal,

Estrada

2021

Preprint

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The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases. While identifying the bulk of the optic disc in a color fundus image is straightforward, accurately segmenting its boundary at the pixel level is very challenging. In this work, we propose disc-centered patch augmentation (DCPA)-a simple, yet novel training scheme for deep neural networks-to address this problem. DCPA achieves state-of-the-art results on full-size images even when using small neural networks, specifically a U-Net with only 7 million parameters as opposed to the original 31 million. In DCPA, we restrict the training data to patches that fully contain the optic nerve. In addition, we also train the network using dynamic cost functions to increase its robustness. We tested DCPA-trained networks on five retinal datasets: DRISTI, DRIONS-DB, DRIVE, AV-WIDE, and CHASE-DB. The first two had available optic disc ground truth, and we manually estimated the ground truth for the latter three. Our approach achieved state-of-the-art F1 and IOU results on four datasets (95% F1, 91% IOU on DRISTI; 92% F1, 84% IOU on DRIVE; 83% F1, 71% IOU on AV-WIDE; 83% F1, 71% IOU on CHASEDB) and competitive results on the fifth (95% F1, 91% IOU on DRIONS-DB), confirming its generality. Our open-source code and ground-truth annotations are

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