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A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI Brain Images

A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI Brain Images

Leena Chandrashekar, Sreedevi A.
Copyright: © 2018 |Volume: 8 |Issue: 3 |Pages: 15
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781522545736|DOI: 10.4018/IJCVIP.2018070101
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MLA

Chandrashekar, Leena, and Sreedevi A. "A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI Brain Images." IJCVIP vol.8, no.3 2018: pp.1-15. http://doi.org/10.4018/IJCVIP.2018070101

APA

Chandrashekar, L. & Sreedevi A. (2018). A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI Brain Images. International Journal of Computer Vision and Image Processing (IJCVIP), 8(3), 1-15. http://doi.org/10.4018/IJCVIP.2018070101

Chicago

Chandrashekar, Leena, and Sreedevi A. "A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI Brain Images," International Journal of Computer Vision and Image Processing (IJCVIP) 8, no.3: 1-15. http://doi.org/10.4018/IJCVIP.2018070101

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Abstract

Estimating the type, size, location and spread of a brain tumor is vital in the diagnosis and treatment of brain cancer. Fused CT and MRI brain images assist in faster detection and diagnosis of brain tumors. They provide superior results in comparison to individual CT or MRI images. Multiscale transforms (MSTs) are widely used in fusing multimodal images like CT and MRI. However, they have a few drawbacks like reduced contrast, poor edge detection, redundancy and high computation time. This article describes how MSTs coupled with sparse representation (SR) aims to overcome the drawbacks. Non-Subsampled Contourlet Transform (NSCT) is widely used on MSTs for fusing multifocal images. Therefore, a novel technique using NSCT and SR is proposed for better quality fused CT and MRI images. The experimental results show superior performance.

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