  The brain perfusion ROI detection being a preliminary step, designed to
exclude non-brain tissues from analyzed DSC perfusion MR images. Its accuracy
is considered as the key factor for delivering correct results of perfusion
data analysis. Despite the large variety of algorithms developed on brain
tissues segmentation, there is no one that works reliably and robustly on
2T-waited MR images of a human head with abnormal brain anatomy. Therefore,
thresholding method is still the state-of-the-art technique that is widely used
as a way of managing pixels involved in brain perfusion ROI. This paper
presents the analysis of effectiveness of thresholding techniques in brain
perfusion ROI detection on 2T-waited MR images of a human head with abnormal
brain anatomy. Four threshold-based algorithms implementation are considered:
according to Otsu method as global thresholding, according to Niblack method as
local thresholding, thresholding in approximate anatomical brain location, and
brute force thresholding. The analysis is done using comparison of qualitative
maps produced from thresholded images and from the reference ones. Pearson
correlation analysis showed strong positive (r was ranged from 0.7123 to
0.8518, p<0.01) and weak positive (r<0.35, p<0.01) relationship in case of
conducted experiments with CBF, CBV, MTT and Tmax maps, respectively. Linear
regression analysis showed at level of 95% confidence interval that maps
produced from thresholded images were subject to scale and offset errors in all
conducted experiments. The experimental results showed that widely used
thresholding methods are an ineffective way of managing pixels involved in
brain perfusion ROI. Thresholding as brain segmentation tool can lead to poor
placement of perfusion ROI and, as a result, produced maps will be subject to
artifacts and can cause falsely high or falsely low perfusion parameters
assessment.
