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

Abstract

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 efficient 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 framework. The local region is defined by the segmentation boundary which, in the case of level set method, consists of global statistics and local energies of every 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 proposed methodology provides similarity between segmented and truth image up to 89.5% by dice method, and minimum distance of 0.5(mm) by Hausdorff algorithm. This adaptive stochastic segmentation algorithm can also be used to compute segmentation when binary thresholding level is greater than 0.2.