Robust segmentation of optic disc and optic cup using statistical Kurtosis test

Abstract

Glaucoma is a chronic and irreversible eye disease that leads to the loss of vision. Evaluation of the Cup to Disc Ratio (CDR) plays a prominent role in the early detection of glaucoma. This paper presents a novel algorithm to compute the CDR for the fundus images. In order to calculate the CDR, the vertical diameter of Optic Disc (OD) and the vertical diameter of the Optic Cup (OC) are calculated from the segmented OD and segmented OC, respectively. This study presents OD and OC segmentation algorithms based on a new statistics‐based kurtosis test. A unique OC boundary segmentation method is presented, which is the combination of partial cup extraction and the cup boundary under the Central Retinal Blood vessels (CRBV). A novel preprocessing technique is introduced to extract the CRBV from the automated Region of Interest (ROI). The experimental results confirm that the proposed algorithm outperforms the state‐of‐art OD and OC segmentation on the three publicly available datasets: ORIGA, DIARETDB0, and DIARETDB1. The proposed OD and OC segmentation achieve accuracies of up to 0.99 and 0.97, respectively. In addition, the proposed model achieves excellent CDR evaluation with an average error percentage reduced to up to 9.6496 for the considered datasets.

Shell‐Net: A robust deep neural network for the joint segmentation of retinal fragments

“…In recent times, several algorithms were implemented to segment the OC and OD from the retinal surface. Biswal et al 42,43 developed two robust approaches to segment the OD and OC using wavelets and kurtosis tests. Cheng et al 44 employed structure-preserving guided retinal image filtering (SGRIF), which is a global edgepreserving smoothing algorithm to detect OD.…”

Automatic Segmentation of Nonstationary EM Emission of Electronics Product

“…The greater the kurtosis value, the sharper the curve. The kurtosis is calculated using the ratio of the fourth-order moment to the square of the second-order moment [77].…”

“…Mathematically, the STK is defined as the kurtosis of the signal frame of interest [77], [78]: where: m x f is the sample mean of the corresponding signal frame; s x f is the standard deviation of the corresponding signal frame.…”