TITLE:
Segmentation of Hyper-Acute Ischemic Infarcts from Diffusion Weighted Imaging Based on Support Vector Machine
AUTHORS:
Yuqing Peng, Xiaodong Zhang, Qingmao Hu
KEYWORDS:
Ischemic Stroke, Infarct Segmentation, Feature Selection, SVM
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.11,
November
19,
2015
ABSTRACT: Accurate and automatic
segmentation of hyper-acute ischemic infarct from magnetic resonance imaging is
of great importance in clinical trials. Manual delineation is labor intensive,
exhibits great variability due to unclear infarct boundaries, and most importantly,
is not practical due to urgent time requirement for prompt therapy. In this
paper, segmentation of hyper-acute ischemic infarcts from diffusion weighted
imaging based on Support Vector Machine (SVM) is explored. Experiments showed
that SVM could provide good agreement with manual delineations by experienced
experts to achieve an average Dice coefficient of 0.7630.121. The proposed
method could achieve significantly higher segmentation accuracy and could be a
potential tool to assist clinicians for quantifying hyper-acute infarction and
decision making especially for thrombolytic therapy.