Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map. (12th March 2014)

• Record Type: Journal Article
• Authors: 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
• Other Names: Pengas George Academic Editor.
• Is Part Of: BioMed research international. Volume 2014(2014)
• Publication Date: 2014-03-12
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2014/963032
• Languages: English
• ISSNs: 2314-6133

Abstract

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.