Abstract:
The detection of brain cancer without human
interfering is a major problem in the domain of
medicinal image processing. The segmentation of
brain images of MRI is a technique used as a first
step to extract different characteristics of these
images for analysis, appreciative and understanding.
The main function of brain segmentation by MRI is to
detect the type of brain abnormality. Many
segmentation techniques are proposed in the
literature. In this comparative paper, we will discuss
the behaviors of tested segmentation methods. Otsu
thresholding, Region growing, Particle swarm
optimization and Interactive graph cut segmentation
methods are analyzed and compared in this paper.
After segmented with these methods, the
morphological operation is used to get exact shape
and size of tumors. As a benchmark dataset, BRATS
dataset is used to test segmentation results.
