Segmentation of Brain from MRI Head Images
Using Modified Chan-Vese Active Contour
Model
Kalavathi
Palanisamy and Somasundram Karuppanagounder
Department of Computer
Science and Applications, Deemed University, India
Abstract: In this article, a new segmentation method to extract the brain from T1,
T2 and PD-weighted Magnetic Resonance Image (MRI) of human head images based on
Modified Chan-Vese (MCV) active contour model is proposed. This method first
segment the brain in the middle slice of the brain volume. Then, the brain
regions of the remaining slices are segmented using the extracted middle brain
as a reference. The input brain image is pre-processed to find the rough brain.
The initial contour for the MCV method is drawn at the center of the segmented
rough brain image and is then propagated to reach the brain boundary. The
result of this proposed method is compared with the hand stripped images and
found to produce significant results. The proposed method was tested with 100
volumes of brain images and had accurately segmented the brain regions which
are better than the existing methods such as Brain Extraction Tool (BET), Brain
Surface Extraction (BSE), Watershed Algorithm (WAT), Hybrid Watershed Algorithm
(HWA) and skull stripping using Graph Cuts (GCUT).
Keywords: Brain
segmentation, skull stripping, brain extraction method, active contour,
magnetic resonance image.
Received May 20, 2014; accepted September
9, 2014