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A Content-Based Approach to Medical Images Retrieval

A Content-Based Approach to Medical Images Retrieval

Mana Tarjoman, Emad Fatemizadeh, Kambiz Badie
Copyright: © 2013 |Volume: 8 |Issue: 2 |Pages: 13
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781466632592|DOI: 10.4018/jhisi.2013040102
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MLA

Tarjoman, Mana, et al. "A Content-Based Approach to Medical Images Retrieval." IJHISI vol.8, no.2 2013: pp.15-27. http://doi.org/10.4018/jhisi.2013040102

APA

Tarjoman, M., Fatemizadeh, E., & Badie, K. (2013). A Content-Based Approach to Medical Images Retrieval. International Journal of Healthcare Information Systems and Informatics (IJHISI), 8(2), 15-27. http://doi.org/10.4018/jhisi.2013040102

Chicago

Tarjoman, Mana, Emad Fatemizadeh, and Kambiz Badie. "A Content-Based Approach to Medical Images Retrieval," International Journal of Healthcare Information Systems and Informatics (IJHISI) 8, no.2: 15-27. http://doi.org/10.4018/jhisi.2013040102

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Abstract

Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency.

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