Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method

Zhou, Wei; Wu, Chengdong; Chen, Dali; Yi, Yugen; Du, Wenyou

doi:10.1109/access.2017.2671918

Cited by 85 publications

(39 citation statements)

References 25 publications

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“…Hence many researchers have been focused over developing efficient methodologies to achieve an improved precision of OD boundary extraction. Based on the methodology accomplished at extraction, they are classified as Classifier based methods [6][7][8][9][10], Template matching based methods [11][12][13][14][15], Morphology based methods [16][17][18] and Active Contour Model based methods. Under the first class, i.e., Classifier based methods; a number of methods are developed.…”

Section: Literature Surveymentioning

confidence: 99%

Optic Disk Segmentation through Edge Density Filter in Retinal Images

Reddy*,

Ravindran

2020

IJITEE

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Accurate and Efficient Detection of Optic Disk (OD) is an important task for the diagnosis of various eye related diseases such as Diabetic Retinopathy, Glaucoma, and papilledema in automatic retinal image analysis. This paper presents a novel and robust OD detection framework which starts with the detection of OD pixel, a center of OD region. Next, based on the ODP, a sub-image extraction is accomplished and it is processed for blood vessel removal through morphological technique. Finally, a new filter, called as Edge Density Filter (EDF) is accomplished to find out the extract the OD region in the Blood vessel removed, Edge detected and binarized sub-image. Two publicly available datasets, MESSIDOR and DRIVE were used to evaluate the performance of proposed method and the performance is measured through performance metrics such as Overlap Score, Sensitivity and Accuracy.

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Section: Literature Surveymentioning

confidence: 99%

Optic Disk Segmentation through Edge Density Filter in Retinal Images

Reddy*,

Ravindran

2020

IJITEE

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“…These are a costlier affair and most of the common people cannot effort such examination. Several phases of diabetic retinopathy are projected in the literature [1][2][3] [4]. And most notable amount they include Non-proliferative retinopathy (NPDR) and proliferative retinopathy which are further divided into mild, moderate and severe.…”

Section: Introductionmentioning

confidence: 99%

Identification of Retinopathy Disease using Image Enhancement

Laxmikanth*,

Ravi

2019

IJEAT

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Among the various after affects of prolonged diabetes Diabetic Retinopathy is one of the diseases that get triggered. The number patients with disease are increasing enormously and there methodologies need to be developed for early detection of the disease. The main cause for this disease is the high blood sugar levels which affects of the blood vessels of retina and there by leading towards blindness. Therefore in this article it reports a methodology based on linear discriminant analysis to combat the disease. The accuracy is estimated using precision and recall, linear discriminant analysis.

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“…Computer based retinal image analysis was first implemented in 1974 [1] and it is now becoming a mainstream technique for quick and accurate detection of retinal diseases such as diabetic retinopathy (DR) and glaucoma [2]. Several important anatomical features appear in the fundus images, such as the retinal blood vessels, the optic disc, and the fovea, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

Zhou

Wu²,

et al. 2018

Computational and Mathematical Methods in Medicine

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The optic disc is a key anatomical structure in retinal images. The ability to detect optic discs in retinal images plays an important role in automated screening systems. Inspired by the fact that humans can find optic discs in retinal images by observing some local features, we propose a local feature spectrum analysis (LFSA) that eliminates the influence caused by the variable spatial positions of local features. In LFSA, a dictionary of local features is used to reconstruct new optic disc candidate images, and the utilization frequencies of every atom in the dictionary are considered as a type of “spectrum” that can be used for classification. We also employ the sparse dictionary selection approach to construct a compact and representative dictionary. Unlike previous approaches, LFSA does not require the segmentation of vessels, and its method of considering the varying information in the retinal images is both simple and robust, making it well-suited for automated screening systems. Experimental results on the largest publicly available dataset indicate the effectiveness of our proposed approach.

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Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method

Cited by 85 publications

References 25 publications

Optic Disk Segmentation through Edge Density Filter in Retinal Images

Optic Disk Segmentation through Edge Density Filter in Retinal Images

Identification of Retinopathy Disease using Image Enhancement

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

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