CFLHCF: Simultaneous Detection of the Optic Disc and Exudates Using Color Features, Local Homogeneity and Contextual Features

Abstract

Detecting Optic Disc (OD) and Exudates (EXs) in the fundus images has been challenging and demanding for the computer-aided diagnosis system. Existing algorithms for detecting OD and EXs are mainly based on traditional learning methods that heavily rely on enhanced OD and EXs features. Unlike traditional learning methods, a novel simultaneous detection of OD and EXs is presented. In this proposed novel Color Features, Local Homogeneity and Contextual Features (CFLHCF), the input original fundus image is preprocessed by color normalization, contrast enhancement, noise removal, and OD localization which the EXs is differentiated from its background information. Then, the preprocessed images are given as the input to Mathematical Morphology Binary Segmentation (MMBS) with Sobel Edge Detection (SED) technique, which detects the EXs from the given fundus images. An MMBS with a SED technique is implemented to boost a highly accurate segmented model for small EXs regions of EXs. The DiaretDB0, DiaretDB1, and STARE datasets are used to validate the proposed method. On the DiaretDB0 dataset, this technique archived an average sensitivity of 98.44% for EXs and a specificity of 98.72% for non‒EXs, which can potentially classify EXs even when the regions are trivial. With respect to sensitivity and specificity values, this method outperformed the previous state‒of‒the‒art methods by roughly 1.24% and 1.09% in the detection of EXs. Additionally, we show the EXs‒diagnosis in ~7 seconds per image.