Identification of Ischemic Stroke by Marker
Controlled Watershed Segmentation
and Fearture
Extraction
Mohammed Ajam, Hussein Kanaan, Lina El Khansa, and Mohammad Ayache
Department of
Biomedical Engineering, Islamic University of Lebanon Beirut, Lebanon
Abstract: In this paper, we will describe a method that
distinguishes the ischemic stroke from Computed Tomography (CT) brain images by
extracting the statistical and textural features. First, preprocessing of the
CT images is done followed by image enhancement. Segmentation of the CT images
is performed by Marker Controlled Watershed. After the segmentation, we get the
Grey Level Co-occurrence matrix (GLCM) and extract the textural and statistical
features. The disadvantage of watershed is the over-segmentation caused by
noise and solved by Marker Controlled Watershed as shown experimentally. The
features extracted are contrast, correlation, standard deviation, variance,
homogeneity, energy and mean. We noticed in our results that the values of
homogeneity, energy and mean are bigger in normal CT images than in abnormal CT
images where the contrast, correlation, standard deviation and variance of normal
CT images are less than those of abnormal CT images (Ischemic Stroke).
Keywords: Ischemic Stroke, Watershed, Grey Level
Co-occurrence Matrix, Textural and Statistical features.
Received February 27, 2020; accepted June 9, 2020