NDLI: Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map

Content Provider	PubMed Central
Author	Tsai, Jang-zern Peng, Syu-jyun Chen, Yu-wei Wang, Kuo-wei Wu, Hsiao-kuang Lin, Yun-yu Lee, Ying-ying Chen, Chi-jen Lin, Huey-juan Smith, Eric Edward Yeh, Poh-shiow Hsin, Yue-loong
Copyright Year	2014
Abstract	Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation.
Related Links	http://dx.doi.org/10.1155/2014/963032
Starting Page	963032
File Format	PDF
ISSN	23146141
e-ISSN	23146141
Journal	BioMed Research International
Volume Number	2014
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2014-01-01
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology

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