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Semi-automatic Segmentation of Brain Tumors Using Population and Individual Information.
MedLine Citation:
PMID:  23319111     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.
Authors:
Yao Wu; Wei Yang; Jun Jiang; Shuanqian Li; Qianjin Feng; Wufan Chen
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-15
Journal Detail:
Title:  Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology     Volume:  -     ISSN:  1618-727X     ISO Abbreviation:  J Digit Imaging     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9100529     Medline TA:  J Digit Imaging     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
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