Robust segmentation of exudates from retinal surface using M-CapsNet via EM routing. (July 2021)

• Record Type: Journal Article
• Authors: Biswal, B.; P, Geetha Pavani; T, Prasanna; karn, Prakash Kumar
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Matrix capsule -- Expectation-maximization (EM) routing -- Exudate segmentation -- Retinopathy -- M-CapsNet; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102770
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper proposes a novel automatic segmentation of exudates using a deep M-CapsNet using Expectation-Maximization Routing. In M-CapsNet, every child capsule connects with every parent capsule at every location for forward prediction of information. The optic disc detected by M-CapsNet is eliminated from the segmented output using regional and morphological features. This paper achieved an average accuracy of 94%, the specificity of 100%, the sensitivity of 100%, and the F1 score of 95%. Abstract: Retinopathy is any damage to the retina of the eyes, which causes vision impairment and may lead to blindness. The initial manifestation of retinopathy is identified by the presence of exudates, microaneurysms on the retinal surface. So, the early detection of exudates prevents the further spread and simultaneously reduces the severity of the disease. However, automatic detection of exudates is a challenging task as the exudates vary from each other in terms of shape and size. This paper proposes a novel approach for the automatic segmentation of exudates using an encoder-decoder style network termed as "deep M-CapsNet using Expectation-Maximization (EM) Routing, " which reduces the memory allocation problems in semantic segmenting of objects. In M-CapsNet, every child capsule connects with every parent capsule at every location. Thus the predictions are forwarded to parent capsules using a shared kernel through a matrix capsule. Due to similar intensities between exudates and the optic disc, the M-CapsNet extracts the exudates along with the optic disc from the retinal surface. The optic disc is eliminated from the segmented output using regional and morphological features. This paper achieves an average accuracy of 94%, the specificity of 100%, the sensitivity of 100%, and the F1 score of 95% when tested over the images selected from publicly available datasets randomly. The experiment results demonstrate that M-CapsNet outperformed previous networks in detecting exudates.