  This paper presents a new approach for relatively accurate brain region of
interest (ROI) detection from dynamic susceptibility contrast (DSC) perfusion
magnetic resonance (MR) images of a human head with abnormal brain anatomy.
Such images produce problems for automatic brain segmentation algorithms, and
as a result, poor perfusion ROI detection affects both quantitative
measurements and visual assessment of perfusion data. In the proposed approach
image segmentation is based on CUSUM filter usage that was adapted to be
applicable to process DSC perfusion MR images. The result of segmentation is a
binary mask of brain ROI that is generated via usage of brain boundary
location. Each point of the boundary between the brain and surrounding tissues
is detected as a change-point by CUSUM filter. Proposed adopted CUSUM filter
operates by accumulating the deviations between the observed and expected
intensities of image points at the time of moving on a trajectory. Motion
trajectory is created by the iterative change of movement direction inside the
background region in order to reach brain region, and vice versa after boundary
crossing. Proposed segmentation approach was evaluated with Dice index
comparing obtained results to the reference standard. Manually marked brain
region pixels (reference standard), as well as visual inspection of detected
with CUSUM filter usage brain ROI, were provided by experienced radiologists.
The results showed that proposed approach is suitable to be used for brain ROI
detection from DSC perfusion MR images of a human head with abnormal brain
anatomy and can, therefore, be applied in the DSC perfusion data analysis.
