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A Data Mining Approach for Compressed Medical Image Retrieval

by Vamsidhar Enireddy, Kiran Kumar Reddi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 5
Year of Publication: 2012
Authors: Vamsidhar Enireddy, Kiran Kumar Reddi
10.5120/8199-1591

Vamsidhar Enireddy, Kiran Kumar Reddi . A Data Mining Approach for Compressed Medical Image Retrieval. International Journal of Computer Applications. 52, 5 ( August 2012), 26-30. DOI=10.5120/8199-1591

@article{ 10.5120/8199-1591,
author = { Vamsidhar Enireddy, Kiran Kumar Reddi },
title = { A Data Mining Approach for Compressed Medical Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 5 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number5/8199-1591/ },
doi = { 10.5120/8199-1591 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:29.843099+05:30
%A Vamsidhar Enireddy
%A Kiran Kumar Reddi
%T A Data Mining Approach for Compressed Medical Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 5
%P 26-30
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The digital medical images are stored in large databases for easy accessibility and Content based image retrieval (CBIR) is used to retrieve diagnostic cases similar to the query medical image. Image compression condense the amount of data required to represent an image, it reduces the storage and transmission requirements. The medical image retrieval problem for compressed images is studied in this paper. The proposed method integrates image retrieval to retrieve diagnostic cases similar to the query medical image and image compression techniques to minimize the bandwidth utilization. Haar wavelet is used for image compression without losses. Edge and texture features are extracted from the medical compressed medical images using Sobel edge detector and Gabor transforms respectively. The classification accuracy of retrieval is evaluated using Naïve Bayes and Support Vector Machine.

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Index Terms

Computer Science
Information Sciences

Keywords

Medical Images Haar Wavelet Sobel Edge detector Gabor filter Support Vector Machine Naïve Bayes

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