Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy

Abstract

Medical image analysis is a very popular research area these days in which digital images are analyzed for the diagnosis and screening of different medical problems. Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness. An automated system for early detection of DR can save a patient's vision and can also help the ophthalmologists in screening of DR. The background or nonproliferative DR contains four types of lesions, i.e., microaneurysms, hemorrhages, hard exudates, and soft exudates. This paper presents a method for detection and classification of exudates in colored retinal images. We present a novel technique that uses filter banks to extract the candidate regions for possible exudates. It eliminates the spurious exudate regions by removing the optic disc region. Then it applies a Bayesian classifier as a combination of Gaussian functions to detect exudate and nonexudate regions. The proposed system is evaluated and tested on publicly available retinal image databases using performance parameters such as sensitivity, specificity, and accuracy. We further compare our system with already proposed and published methods to show the validity of the proposed system.

An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

“…In [7] have proposed a system to extract candidate regions for possible HE using filter banks. The proposed approach makes use of three stages: the first stage is the retinal image acquisition and pre-processing, second stage is the candidate exudate region detection and elimination of the optic disc and the last stage is the feature set formulation and classification of regions as exudate or non-exudate regions.…”

An Automated Diabetic Retinopathy Classification System Using Bayesian Logistic Regression Classifier

“…Contrast Limited adaptive histogram equalization technique is used [11] to highlight the MA and other lesions in a better way. For detecting the edges, Laplacian of Gaussian (LoG) [12] function is used to mark the edges as it highlights the regions where the intensity changes rapidly and as the image was already smoothened using image enhancing techniques. Segmentation: The separation of blood vessels, MA and other retinal structures is a vital step in the DR detection.…”

A Case Study Approach: Iterative Prototyping Model Based Detection of Macular Edema in Retinal OCT Images

“…There is no work done already for Fovea and Edema detection using OCT images but there are many algorithms proposed by many researchers for fovea and edema detection in Digital Fundus Images [13][14][15][16]. One is the detection of fovea in fundus images is by using some morphological operation of Image processing.…”