Adaptive stochastic segmentation via energy-convergence for brain tumor in MR images


An adaptive algorithm that formulates an energy based stochastic segmentation with a level set methodology is proposed.The hybrid method uses global and local energies, which are ecient in matching, segmenting and tracing anatomic structures by exploiting constraints computed from the data of the image. The algorithm performs autonomous stochastic segmentation of tumor in Magnetic Resonance Imaging (MRI) by combining region based level sets globally and three established energies (uniform, separation and histogram) in a local frame- work. The local region is de ned by the segmentation boundary which, in the case of level set method, consists of global statistics and local energies of ev- ery individual point and the local region is then updated by minimizing (or maximizing) the energies. For analysis, the algorithm is tested on low grade and high grade MR images dataset. The obtained results show that the pro- posed methodology provides similarity between segmented and truth image up to 89:5% by dice method, and minimum distance of 0.5(mm) by Hausdor al- gorithm. This adaptive stochastic segmentation algorithm can also be used to compute segmentation when binary thresholding level is greater than 0.2.

Keywords: Active contours, level set,statistical energies,stochastic segmentation, MR images.