Comparative Evaluation of Thresholding and Segmentation Algorithms

Segmentation of brain tumor manually consumes more time and it is a challenging task. This paper detects the tumor inside the brain by doing segmentation and extraction of the tumor which is been detected. To prove the efficiency of the detection of brain tumor we have performed a comparative study of two segmentation algorithms namely “watershed segmentation algorithm” and “k-means clustering segmentation algorithm”. After the segmentation process the various morphological operations are applied on the segmented image. The morphological operations are applied to concentrate only on the required tumor part and ignoring the remaining area in the brain. The various thresholding algorithms like “Otsu’s thresholding” and “brute force thresholding” is applied to improve the efficiency of the final output image. Comparative study is made between the segmentation algorithms and the thresholding algorithms used. The further step of this project is to present an analytical method to detect tumors in medical images for 3D representation or visualization. Keywords— MRI, Tumor, Segmentation, Thresholding, Morphological Operation, 3D Visualization.