Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow

TL;DR

This paper introduces an automatic brain tumor segmentation method combining a normalized Gaussian Bayesian classifier with a novel 3D Fluid Vector Flow algorithm, improving accuracy in MRI analysis.

Contribution

It proposes a new NGMM for modeling healthy brain tissues and extends the 2D FVF algorithm to 3D for effective tumor segmentation.

Findings

Validated on a public dataset with promising results

Achieved accurate segmentation of brain tumors

Enhanced 3D segmentation techniques

Abstract

Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an important task to measure tumor responses to treatments. However, automatic segmentation is very challenging. This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm. In our method, a Normalized Gaussian Mixture Model (NGMM) is proposed and used to model the healthy brain tissues. Gaussian Bayesian Classifier is exploited to acquire a Gaussian Bayesian Brain Map (GBBM) from the test brain MR images. GBBM is further processed to initialize the 3D FVF algorithm, which segments the brain tumor. This algorithm has two major contributions. First, we present a NGMM to model healthy brains. Second, we extend our 2D FVF algorithm to 3D space and use it for brain tumor segmentation. The proposed method is validated on a publicly available dataset.