Application Of Fuzzy System In Segmentation Of MRI Brain Tumor

TL;DR

This paper presents a fuzzy system-based approach for segmenting MRI brain tumors, utilizing level set evolution with statistical forces to improve accuracy in challenging tumor geometries.

Contribution

It introduces a novel combination of fuzzy systems and level set evolution with statistical forces for more reliable MRI tumor segmentation.

Findings

Effective segmentation of brain tumors in MRI images.

Overcomes boundary leakage issues with statistical force integration.

Achieves stable convergence in tumor boundary detection.

Abstract

Segmentation of images holds an important position in the area of image processing. It becomes more important whi le typically dealing with medical images where presurgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process. Segmentation of 3-D tumor structures from magnetic resonance images (MRI) is a very challenging problem due to the variability of tumor geometry and intensity patterns. Level set evolution combining global smoothness with the flexibility of topology changes offers significant advantages over the conventional statistical classification followed by mathematical morphology. Level set evolution with constant propagation needs to be initialized either completely inside or outside the tumor and can leak through weak or missing boundary parts. Replacing the constant propagation term by a statistical force overcomes these limitations and results in a convergence to a stable solution. Using MR images presenting tumors, probabilities for background and tumor regions are calculated from a pre- and post-contrast difference image and mixture modeling fit of the histogram. The whole image is used for initialization of the level set evolution to segment the tumor boundaries.