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Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

Jafari, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1117/12.921075
  3. Abstract:
  4. 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
  5. Keywords:
  6. Artificial neural network classifier ; Artificial Neural Network ; Back-propagation neural networks ; Brain tissue ; Brain tissue segmentation ; Classification accuracy ; Coding standards ; Gray matter ; H.264/AVC ; Intraframe coding ; Medical images ; MR images ; Network-based ; Tumor tissues ; White matter ; Brain ; Cerebrospinal fluid ; Codes (symbols) ; Compression ratio (machinery) ; Computational complexity ; Feature extraction ; Image coding ; Image segmentation ; Motion Picture Experts Group standards ; Neural networks ; Tissue ; Computer vision
  7. Source: Proceedings of SPIE - The International Society for Optical Engineering, 9 December 2011 through 10 December 2011, Singapore ; Volume 8349 , December , 2012 ; 0277786X (ISSN) ; 9780819490254 (ISBN)
  8. URL: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1233068