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Retinal Blood Vessel Segmentation through Morphology Cascaded Features and Supervised Learning

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Title Retinal Blood Vessel Segmentation through Morphology Cascaded Features and Supervised Learning
 
Creator Suhasini Devi, Y Aruna
Chari Kamsali, Manjunatha
 
Subject Classification
Feature extraction
Minor vessels
Morphology
Post processing
 
Description 264-273
Retinal blood vessels are the most important attributes in the automatic diagnosis of Diabetic Retinopathy (DR). Since
the advanced stages of DR are diagnosed through blood vessels, their segmentation followed by clear analysis is required.
Such process can be accompanied through the classification. Segmentation of minor and thin vessels is a challenge because
they are analogous to background pixels in fundus image. To solve this issue, this paper proposes a three-stage retinal vessel
segmentation mechanism from fundus images. In the first stage, the fundus image is pre-processed for enhancement and
then major blood vessels are processed, after extracting them through filtering and morphological transformation. Each pixel
of the resulting image is represented by a set of composite features and then processed for pixel level classification. A total
of five different features are used to signify each pixel and then for classification Support Vector Machine (SVM) algorithm
is used. In the final and post-processing stage, the outputs of first two stages are fused to get the complete retinal vessel
structure. Using DRIVE dataset, the proposed method’s experimental validation proves the effectiveness of segmentation
accuracy and computational time. The average improvement in the Accuracy, Specificity and Sensitivity is observed as
2.3645%, 1.3365% and 5.2314% respectively from past recent vessel segmentation methods.
 
Date 2024-03-06T11:40:43Z
2024-03-06T11:40:43Z
2024-03
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63544
https://doi.org/10.56042/jsir.v83i3.1498
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.83(3) [March 2024]