  Medical image segmentation being a substantial component of image processing
plays a significant role to analyze gross anatomy, to locate an infirmity and
to plan the surgical procedures. Segmentation of brain Magnetic Resonance
Imaging (MRI) is of considerable importance for the accurate diagnosis.
However, precise and accurate segmentation of brain MRI is a challenging task.
Here, we present an efficient framework for segmentation of brain MR images.
For this purpose, Gabor transform method is used to compute features of brain
MRI. Then, these features are classified by using four different classifiers
i.e., Incremental Supervised Neural Network (ISNN), K-Nearest Neighbor (KNN),
Probabilistic Neural Network (PNN), and Support Vector Machine (SVM).
Performance of these classifiers is investigated over different images of brain
MRI and the variation in the performance of these classifiers is observed for
different brain tissues. Thus, we proposed a rule-based hybrid approach to
segment brain MRI. Experimental results show that the performance of these
classifiers varies over each tissue MRI and the proposed rule-based hybrid
approach exhibits better segmentation of brain MRI tissues.
