Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Abstract

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection.

Classification of Diabetic Retinopathy Images by Using Deep Learning Models

“…Fuzzy C-means is a standard clustering mechanism works very well to identify the membership(µij) information form the image [19]. The cluster center updatation formula: (4) And the centers (vj) gets updated according to: (5) By using FCM clusterng the given level and clustered image levels have been compared to make the training levels valid. The idea behind using FCM is because of the data points are not mandatory to be under one cluster center, here data points are given to the membership of each cluster centers, because of this nature one data point can be under multiple cluster centers [20][21].…”

Automated retinal lesion detection via image saliency analysis

“…Rabbani et al employed an active contour segmentation model to detect the boundaries of LK in FA images of subjects with diabetic macular edema. Zhao et al used the intensity and compactness features to generate a saliency map, and segment the precise LK area by using a graph‐cut model. Liu et al presented a location‐to‐segmentation strategy for automatic EX segmentation in color retinal fundus images.…”

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

“…However, many of the improved saliency segmentation algorithms still face difficulty when salient objects share similar color features with the background pixels. These algorithms often lack the ability to effectively handle complicated images with low contrast [18,20,37]. Complementing the methods of saliency computation with other useful analysis methods such as the morphological analysis can significantly improve image segmentation results.…”

“…The method of saliency based segmentation has emerged as an important tool for medical image analysis because of its capability to identify salient objects in images [17,18]. Its application in computer vision is largely inspired by the findings that human vision perception has a higher probability to focus on the part of an image that carries useful information [17,19].…”