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Content based human retinal image retrieval using vascular feature extraction
Published in
Volume: 7197 LNAI
Issue: PART 2
Pages: 468 - 476
In this work, an attempt has been made to analyze retinal images for Content Based Image Retrieval (CBIR) application. Different normal and abnormal images are subjected to vessel detection using Canny based edge detection method with and without preprocessing. Canny segmentation using morphological preprocessing is compared with conventional Canny without preprocessing and contrast stretching based preprocessing method. Essential features are extracted from the segmented images. The similarity matching is carried out between the features obtained from the query image and retinal images stored in the database. The best matched images are ranked and retrieved with appropriate assessment. The results show that it is possible to differentiate the normal and abnormal retinal images using the features derived using Canny with morphological preprocessing. The recall of this CBIR system is found to be 82% using the Canny with morphological preprocessing and is better than the other two methods. It appears that this method is useful to analyze retinal images using CBIR systems. © 2012 Springer-Verlag.
About the journal
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Open AccessNo
Concepts (11)
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    Content based image retrieval
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    Content based retrieval
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    Database systems
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    Edge detection
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    Feature extraction
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    Image processing