Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
This work addresses the problem of automating perfusion analysis for clinicians dealing with abnormal brain anatomy, but it appears incremental as it builds on existing segmentation techniques with a specific filter application.
The paper tackles brain tissue segmentation on T2-weighted MR perfusion images with abnormal anatomy, proposing a fully automated method using a CUSUM filter for boundary detection, which significantly reduces time and effort for perfusion region of interest detection in 20 clinical cases.
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.