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BioMed Research International
Volume 2014 (2014), Article ID 963032, 13 pages
http://dx.doi.org/10.1155/2014/963032
Research Article

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

1Department of Electrical Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan
2Department of Computer Science and Information Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan
3Department of Neurology, Landseed Hospital, Pingzhen City, Taoyuan County 32449, Taiwan
4Department of Neurology, National Taiwan University Hospital, Taipei City 10002, Taiwan
5Department of Medical Imaging, Landseed Hospital, Pingzhen City, Taoyuan County 32449, Taiwan
6Department of Radiology, Taipei Medical University-Shuang Ho Hospital, New Taipei City 23561, Taiwan
7Department of Neurology, Chi-Mei Medical Center, Tainan City 71004, Taiwan
8Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada T2N 1N4
9Epilepsy Center, Buddhist Tzu Chi General Hospital, Hualian City, Hualian County 97002, Taiwan
10Biomedical Electronics Translational Research Center, National Chiao Tung University, Hsinchu City 30010, Taiwan
11Department of Neurology, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan

Received 5 November 2013; Revised 31 December 2013; Accepted 9 January 2014; Published 12 March 2014

Academic Editor: George Pengas

Copyright © 2014 Jang-Zern Tsai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.