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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dc.contributor.authorTsai, Jang-Zern
dc.contributor.authorPeng, Syu-Jyun
dc.contributor.authorChen, Yu-Wei
dc.contributor.authorWang, Kuo-Wei
dc.contributor.authorWu, Hsiao-Kuang
dc.contributor.authorLin, Yun-Yu
dc.contributor.authorLee, Ying-Ying
dc.contributor.authorChen, Chi-Jen
dc.contributor.authorLin, Huey-Juan
dc.contributor.authorSmith, Eric Edward
dc.contributor.authorYeh, Poh-Shiow
dc.contributor.authorHsin, Yue-Loong
dc.date.accessioned2018-09-27T11:33:44Z
dc.date.available2018-09-27T11:33:44Z
dc.date.issued2014-03-12
dc.date.updated2018-09-27T11:33:43Z
dc.description.abstractDetermination 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.
dc.description.versionPeer Reviewed
dc.identifier.citationJang-Zern Tsai, Syu-Jyun Peng, Yu-Wei Chen, et al., “Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map,” BioMed Research International, vol. 2014, Article ID 963032, 13 pages, 2014. doi:10.1155/2014/963032
dc.identifier.doihttps://doi.org/10.1155/2014/963032
dc.identifier.urihttp://hdl.handle.net/1880/108200
dc.identifier.urihttps://doi.org/10.11575/PRISM/45691
dc.language.rfc3066en
dc.rights.holderCopyright © 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.
dc.titleAutomatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map
dc.typeJournal Article
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