Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels

TL;DR

This paper introduces an automated brain tissue segmentation method on MR perfusion images using CUSUM filter to accurately identify boundary pixels, aiding perfusion analysis in abnormal brain anatomy.

Contribution

The novel approach applies CUSUM filter for boundary detection, enabling automated segmentation on T2-weighted perfusion images with improved efficiency.

Findings

Significantly reduces segmentation time and effort.

Effective on images with abnormal brain anatomy.

Validated on 20 clinical cases.

Abstract

The fully automated and relatively accurate method of brain tissues segmentation on T2-weighted magnetic resonance perfusion images is proposed. Segmentation with this method provides a possibility to obtain perfusion region of interest on images with abnormal brain anatomy that is very important for perfusion analysis. In the proposed method the result is presented as a binary mask, which marks two regions: brain tissues pixels with unity values and skull, extracranial soft tissue and background pixels with zero values. The binary mask is produced based on the location of boundary between two studied regions. Each boundary point is detected with CUSUM filter as a change point for iteratively accumulated points at time of moving on a sinusoidal-like path along the boundary from one region to another. The evaluation results for 20 clinical cases showed that proposed segmentation method could significantly reduce the time and efforts required to obtain desirable results for perfusion region of interest detection on T2-weighted magnetic resonance perfusion images with abnormal brain anatomy.