Segmentation of Brain Tumor and Performance Evaluation Using Spatial FCM and Level Set Evolution

In recent years, brain tumor is one of the major causes of death in human beings. The survival rate can be increased if the tumor is diagnosed accurately in the early stage. Hence, medical image segmentation is always a challenging task of any problem in computer guided medical procedures in hospitals. The main objective of the segmentation process is to obtain object of interest from the given image so that it can be represented in a meaningful way for further analysis.

To improve the segmentation accuracy, an efficient segmentation method which combines a spatial fuzzy c-means and level sets is proposed in this paper.

The experiment is conducted using brain web and DICOM database. After pre-processing of an MR image, a spatial FCM algorithm is applied. The SFCM utilizes spatial data from the neighbourhood of each pixel to represent clusters. Finally, these clusters are segmented using level set active contour model for the tumor boundary. The performance of the proposed algorithm is evaluated using various performance metrics.

In this technique, wavelets and spatial FCM are applied before segmenting the brain tumor by level sets. The qualitative results show more accurate detection of tumor boundary and better convergence rate of the contour as compared to other segmentation techniques. The proposed segmentation frame work is also compared with two automatic segmentation techniques developed recently. The quantitative results of the proposed method summarize the improvements in segmentation accuracy, sensitivity and specificity.