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Automated Image Annotation for Semantic Indexing and Retrieval of Medical Images

by Krishna A N, B G Prasad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 3
Year of Publication: 2012
Authors: Krishna A N, B G Prasad
10.5120/8736-2843

Krishna A N, B G Prasad . Automated Image Annotation for Semantic Indexing and Retrieval of Medical Images. International Journal of Computer Applications. 55, 3 ( October 2012), 26-33. DOI=10.5120/8736-2843

@article{ 10.5120/8736-2843,
author = { Krishna A N, B G Prasad },
title = { Automated Image Annotation for Semantic Indexing and Retrieval of Medical Images },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 3 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number3/8736-2843/ },
doi = { 10.5120/8736-2843 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:56:19.519318+05:30
%A Krishna A N
%A B G Prasad
%T Automated Image Annotation for Semantic Indexing and Retrieval of Medical Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 3
%P 26-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Medical image retrieval to search for clinically relevant and visually similar images depicting suspecious lesions have been attracting research interest. Content-based image retrieval (CBIR) is an important alternate and complement to traditional text-based retrieval using keywords. We have implemented CBIR system based on effective use of texture information within the images obtained by statistical cooccurrence matrix method. Also, the method is improved by bridging the semantic gap between low-level visual features and the high-level semantic concepts using automated image annotations. In this paper, we have proposed a classification-based multi-class multi-label semantic model and the corresponding learning procedure to address the problem of automatic image annotation using J48 decision tree classifier and show its application to medical image retrieval. Hash structure is used to index images. Eucledian distance measure is used for similarity measurement. Both the methods are compared using precision and recall measures. Semantic indexing is shown to outperform CBIR for MR-T2 axial brain images.

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Index Terms

Computer Science
Information Sciences

Keywords

Cooccurrence matrix Decision tree classifier Semantic indexing. ifx

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