Automated diagnosis system for age‐related macular degeneration using hybrid features set from fundus images

Abstract

Impairment to macula can cause loss of central vision. There are various macular disorders that can affect macular region and if not treated at an early stage can cause irreversible central vision loss. Age‐related macular degeneration (AMD) disorder is one of the most threading macular disorder. Bright lesion, drusens presence in macular region is known as the hallmark of AMD disorder. This bright lesion differentiation from other bright lesion like exudates is important for accurate diagnosis of AMD. Focus of this article is automated diagnosis of affected macular region by applying a hybrid features set containing textural, color, and structural/shape features for more accurate detection of AMD at an early stage using fundus images. These features also help to distinguish drusens from exudates. The proposed algorithm at first stage, detect macular region from input fundus image and then perform features extraction based on textural pattern, edge, and structural properties of macular region to classify abnormal macula from normal macula. For classification, we have used support vector machine (SVM), K‐nearest neighbor and neural networks but SVM classifier achieves high accuracy. The proposed algorithm is tested on publicly available STARE and locally available AFIO datasets. Attained sensitivity, specificity, and accuracy of our proposed system are 97.5%, 95% and 95.45%, respectively, when applied on STARE dataset. When we have applied our proposed system on AFIO dataset, we have attained sensitivity, specificity, and accuracy of 93.3%, 92% and 92.34%, respectively.

Mobile‐aided screening system for proliferative diabetic retinopathy

“…The SVM is an automatic learning algorithm used for data classification, which is widely used for binary classification. 55,57,58 The principal objective of SVM is to determine an optimal hyperplane that separates the training data into classes, by maximizing the geometric margin between each other. [59][60][61] Many related works 12,13,15 used the SVM classifier to detect the NVD where optimal performances have been achieved.…”

The Methods of Determining Temporal Direction Based on Asymmetric Information of the Optic Disc for Optimal Fovea Detection

“…Similarly, Medhi [22] achieved comparable results by using the main blood vessels to identify the fovea region of interest (ROI). Other investigations have utilized the optic disc (OD) to locate the fovea area [19,23]. Chalakkal [19] employed connected component analysis to define the foveal ROI in relation to the OD, while Khalid et al [23] used normalized cross-correlation.…”

“…Other investigations have utilized the optic disc (OD) to locate the fovea area [19,23]. Chalakkal [19] employed connected component analysis to define the foveal ROI in relation to the OD, while Khalid et al [23] used normalized cross-correlation. Additionally, aside from the OD's significance as a reference point, other researchers have emphasized that the localization of the fovea is influenced by the direction of search required.…”