Brain Image Segmentation using Level Set : An Hybrid Approach

Abstract

Advances in medical imaging technologies have given rise for effective diagnostic procedures. The acquisition promptness and resolution enhancements of imaging modalities have given physicians more information, less invasively about their patients. Active contours are used to segment, match and track images of an atomic structure by manipulating constraints derived from the image data together with prior knowledge about the location, size, and shape of these structures. The level set method is referred as a part of active contour family. The major disadvantages of level set method are initialization of controlling parameters and time complexity. The proposed method adopts Robust Spatial Kernel Fuzzy C-Means (RSKFCM) and Lattice Boltzmann Method (LBM) to overcome these drawbacks. RSKFCM is based on standard Fuzzy C-Means algorithm which uses Gaussian RBF kernel function as distance metric and incorporates spatial information. The LBM uses the energy function to determine and reduce the actual processing time which addresses the time complexity. The proposed system combines both RSKFCM and LBM to form a hybrid approach, and the system is tested on a large set of MRI brain images and the experimental results are found to be improved with respect to time complexity.