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Proceedings Paper

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264
Author(s): Mehdi Jafari; Shohreh Kasaei
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Paper Abstract

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Paper Details

Date Published: 13 January 2012
PDF: 5 pages
Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83490S (13 January 2012); doi: 10.1117/12.921075
Show Author Affiliations
Mehdi Jafari, Islamic Azad Univ. (Iran, Islamic Republic of)
Shohreh Kasaei, Sharif Univ. of Technology (Iran, Islamic Republic of)


Published in SPIE Proceedings Vol. 8349:
Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis
Zhu Zeng; Yuting Li, Editor(s)

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