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

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Title:	Automatic detection and quantification of acute cerebral infarct by fuzzy clustering and histographic characterization on diffusion weighted mr imaging and apparent diffusion coefficient map
Authors:	吳曉光;Peng, Syu-Jyun;Tsai, Jang-Zern;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
Contributors:	資訊電機學院資訊工程學系
Keywords:	Accuracy;Aged;Aged, 80 and over;Algorithms;Biomedical research;Brain research;Cerebral Infarction - diagnostic imaging;Cerebral Infarction - pathology;Cerebrovascular disease;Computer engineering;Diffusion;Diffusion Magnetic Resonance Imaging;Female;Heart attacks;Humans;Image Processing, Computer-Assisted;Male;Medical research;Medicine, Experimental;Middle Aged;Mortality;Neural networks;Neurology;NMR;Nuclear magnetic resonance;Patients;Personal computers;Radiography;Stroke;Stroke - diagnostic imaging;Stroke - pathology
Date:	2014-01-01
Issue Date:	2026-04-23 13:18:50 (UTC+8)
Publisher:	Hindawi Publishing Corporation;Cairo, Egypt: Hindawi Puplishing Corporation
Abstract:	摘要： 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. 其他題名： Biomed Res Int 出版者： Cairo, Egypt: Hindawi Puplishing Corporation 出版日期： 2014-01-01 出處： BioMed research international, 2014-01, Vol.2014 (2014), p.1-13 資源來源： Publicly Available Content Database (Proquest) 版權： Copyright © 2014 Jang-Zern Tsai et al. 版權： COPYRIGHT 2014 John Wiley & Sons, Inc. 版權： Copyright © 2014 Jang-Zern Tsai et al. 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. 版權： Copyright © 2014 Jang-Zern Tsai et al. 2014 識別號： ISSN: 2314-6133 識別號： ISSN: 2314-6141 識別號： EISSN: 2314-6141 識別號： DOI: 10.1155/2014/963032 識別號： PMID: 24738080
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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