A novel method for retinal exudate segmentation using signal separation algorithm

Imani, Elaheh; Pourreza, Hamid Reza

doi:10.1016/j.cmpb.2016.05.016

Cited by 72 publications

(31 citation statements)

References 30 publications

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“…This article stated an image level sensitivity and specificity values as 0.92 and 0.68 (on DIARETDB1 database) and 0.87 and 0.86 (on HEI-MED database) and lesion based sensitivity of 0.86 on DIARETDB1 database. Imani.E et al [53] developed novel Morphological Component Analysis (MCA) method to distinguish exudates from blood capillaries and attained a final exudate map using mathematical morphology and dynamic threshold methods. An accuracy of 0.961, 0.948 and 0.937 assessed on DiaretDB (89 images), HEI-MED (169 images) and e-ophtha (82 images) datasets respectively.…”

Section: Literature Review Of Existing Methods and Resultsmentioning

confidence: 99%

Evaluation of Conventional methods for the Detection of Lesions in Diabetic Retinopathy Images: A Research

2019

IJITEE

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 Abstract-Diabetes Mellitus (DM) is a sporadic ailment which arises with the vagaries in blood glucose levels. Prolonged history of DM will result in retinal vasculature impediments and leads to Diabetic Retinopathy (DR). The patho is characterized by leakage of blood, fat and protein based particles into the macula and instigates the vision problems. The reliability Conventional clinician's screening methods is dependent on skilled professionals for diagnosis and screening. It costs to a great deal of time with manual labor and hence there is a great need to automate DR detection. Usage of image processing and machine learning approach to sense various retinopathy aberrations gained huge attraction in recent past. This paper reveals various DR detection and classification methods, including tools, implemented techniques and datasets used. It wishes to help researchers by giving brief literature review of merits and demeritsof existing methods, so that it will help them to plan future developments.

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Section: Literature Review Of Existing Methods and Resultsmentioning

confidence: 99%

Evaluation of Conventional methods for the Detection of Lesions in Diabetic Retinopathy Images: A Research

2019

IJITEE

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“…TN Sp TNR TN+FP = = (10) where TP (True Positives) is the number of abnormal fundus images found as abnormal with exudates pixels correctly detected, TN (True Negatives) is the number of normal fundus images found as normal with non-exudates pixels that are correctly detected as non-exudates pixels, FP (False Positives) is the number of normal fundus images found as abnormal with non-exudates pixels that are wrongly detected as exudates pixels and FN (False Negatives) is the number of abnormal fundus images found as normal with exudates pixel that are not detected as exudates [6].…”

Section: System Performances and Discussionmentioning

confidence: 99%

“…The exudates borders were detected by applying an active contour model, and a Naïve-Bayes classifer is applied to remove the false exudate candidates. Imani and Pourreza [10] presented an automatic method for the detection of retinal exudates. This method lies in the use of Morphological Component Analysis (MCA) algorithm to separate lesions from normal retinal structures to facilitate the detection process.…”

Section: Related Workmentioning

confidence: 99%

Automated Exudates Detection in Retinal Fundus Image Using Morphological Operator and Entropy Maximization Thresholding

Kom¹,

Tindo²,

Pone³

et al. 2019

JBiSE

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Blindness which is considered as degrading disabling disease is the final stage that occurs when a certain threshold of visual acuity is overlapped. It happens with vision deficiencies that are pathologic states due to many ocular diseases. Among them, diabetic retinopathy is nowadays a chronic disease that attacks most of diabetic patients. Early detection through automatic screening programs reduces considerably expansion of the disease. Exudates are one of the earliest signs. This paper presents an automated method for exudates detection in digital retinal fundus image. The first step consists of image enhancement. It focuses on histogram expansion and median filter. The difference between filtered image and his inverse reduces noise and removes background while preserving features and patterns related to the exudates. The second step refers to blood vessel removal by using morphological operators. In the last step, we compute the result image with an algorithm based on Entropy Maximization Thresholding to obtain two segmented regions (optical disk and exudates) which were highlighted in the second step. Finally, according to size criteria, we eliminate the other regions obtain the regions of interest related to exudates. Evaluations were done with retinal fundus image DIARETDB1 database. DIARETDB1 gathers high-quality medical images which have been verified by experts. It consists of around 89 colour fundus images of which 84 contain at least mild non-proliferative signs of the diabetic retinopathy. This tool provides a unified framework for benchmarking the methods, but also points out clear defi

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“…Finally, the random forest classifier was applied on the extracted features, i.e., size, color, and contrast of exudates, and an SE of 83% and an accuracy of 79% was reported using 136 fundus images. Imani et al [97] considered a morphological component analysis (MCA) model to separate retinal vessels from the exudate region using 340 images. Afterwards, mathematical morphology and dynamic thresholding methods were incorporated for the distinguishing of EXs from normal features, yielding AUC of 0.961 score.…”

Section: Exudate Detection Methodsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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A novel method for retinal exudate segmentation using signal separation algorithm

Cited by 72 publications

References 30 publications

Evaluation of Conventional methods for the Detection of Lesions in Diabetic Retinopathy Images: A Research

Evaluation of Conventional methods for the Detection of Lesions in Diabetic Retinopathy Images: A Research

Automated Exudates Detection in Retinal Fundus Image Using Morphological Operator and Entropy Maximization Thresholding

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

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