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Analysis of vasculature in human retinal images using particle swarm optimization based tsallis multi-level thresholding and similarity measures
Published in
2012
Volume: 7677 LNCS
   
Pages: 380 - 387
Abstract
Retinal vasculature of the human circulatory system which can be visualized directly provides a number of systemic conditions and can be diagnosed by the detection of lesions. Changes in these structures are found to be correlated with pathological conditions and provide information on severity or state of various diseases. In this work, particle swarm optimization algorithm based multilevel thresholding is adopted for detecting the vasculature structures in retinal fundus images. Initially, adaptive histogram equalization is used for pre-processing of the original images. Tsallis multilevel thresholding is used for the segmentation of the blood vessels. Further, similarity measures are used to quantify the similarity between the segmented result and the corresponding ground truth. The optimal multi-threshold selection using particle swarm optimization seems to provide better results. Similarity measures analysis using dendrogram and box plot provide validation of the segmentation procedure attempted. © 2012 Springer-Verlag.
About the journal
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN03029743
Open AccessNo
Concepts (22)
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    ADAPTIVE HISTOGRAM EQUALIZATION
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    DENDROGRAMS
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    FUNDUS IMAGE
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    Ground truth
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    HUMAN CIRCULATORY SYSTEMS
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    MULTILEVEL THRESHOLDING
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    MULTITHRESHOLD
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    Original images
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    PARTICLE SWARM OPTIMIZATION ALGORITHM
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    Pathological conditions
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    Pre-processing
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    RETINAL IMAGE
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    RETINAL VASCULATURE
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    SEGMENTATION PROCEDURE
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    Similarity measure
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    Vasculature
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    VASCULATURE STRUCTURES
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    Algorithms
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    Blood vessels
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    Image segmentation
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    OPHTHALMOLOGY
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    Particle swarm optimization (pso)