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Biomed Res Int. 2014;2014:963032. doi: 10.1155/2014/963032. Epub 2014 Mar 12.

Automatic detection and quantification of acute cerebral infarct by fuzzy clustering and histographic characterization on diffusion weighted MR imaging and apparent diffusion coefficient map.

Author information

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

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.

PMID:
24738080
PMCID:
PMC3971548
DOI:
10.1155/2014/963032
[Indexed for MEDLINE]
Free PMC Article

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