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Article:A CBIR System for Human Brain Magnetic Resonance Image Indexing

by Mina Rafi Nazari, Emad Fatemizadeh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 14
Year of Publication: 2010
Authors: Mina Rafi Nazari, Emad Fatemizadeh
10.5120/1327-1636

Mina Rafi Nazari, Emad Fatemizadeh . Article:A CBIR System for Human Brain Magnetic Resonance Image Indexing. International Journal of Computer Applications. 7, 14 ( October 2010), 33-37. DOI=10.5120/1327-1636

@article{ 10.5120/1327-1636,
author = { Mina Rafi Nazari, Emad Fatemizadeh },
title = { Article:A CBIR System for Human Brain Magnetic Resonance Image Indexing },
journal = { International Journal of Computer Applications },
issue_date = { October 2010 },
volume = { 7 },
number = { 14 },
month = { October },
year = { 2010 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume7/number14/1327-1636/ },
doi = { 10.5120/1327-1636 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:56:18.778025+05:30
%A Mina Rafi Nazari
%A Emad Fatemizadeh
%T Article:A CBIR System for Human Brain Magnetic Resonance Image Indexing
%J International Journal of Computer Applications
%@ 0975-8887
%V 7
%N 14
%P 33-37
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content-based image retrieval (CBIR) is becoming an important field with the advance of multimedia and imaging technology everincreasingly. It makes use of image features, such as color, shape and texture, to index images with minimal human intervention. Among many retrieval features associated with CBIR, texture retrieval is one of the most powerful. Content-based image retrieval can also be utilized to locate medical images in large databases. In this research, we introduce a content-based approach to medical image retrieval. A case study, which describes the methodology of a CBIR system for retrieving digital human brain MRI database based on textural features retrieval, is then presented. This research intends to disseminate the knowledge of the CBIR approach to the applications of medical image management and to discrimination between the normal and abnormal medical images based on features. The main indices are finding Normal, Abnormal and clustering the abnormal images to detect two certain abnormalities: Multiple Sclerosis and Tumoral images to classify the database. A classification with a success of 95% has been obtained by the proposed method. This result indicates that the proposed method is robust and effective compared with other recently works.

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Index Terms

Computer Science
Information Sciences

Keywords

Magnetic resonance image Medical image Feature extraction Support vector machine Co-occurrence matrices