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

Jang Zern Tsai, Syu Jyun Peng, Yu Wei Chen, Kuo Wei Wang, Hsiao Kuang Wu, Yun Yu Lin, Ying Ying Lee, Chi Jen Chen, Huey-Juan Lin, Eric Edward Smith, Poh Shiow Yeh, Yue Loong Hsin

Research output: Contribution to journalArticle

15 Citations (Scopus)

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.

Original languageEnglish
Article number963032
JournalBioMed Research International
Volume2014
DOIs
Publication statusPublished - 2014

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Medicine(all)

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