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The brain perfusion ROI detection being a preliminary step, designed toexclude non-brain tissues from analyzed DSC perfusion MR images. Its accuracyis considered as the key factor for delivering correct results of perfusiondata analysis. Despite the large variety of algorithms developed on braintissues segmentation, there is no one that works reliably and robustly on2T-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 usedas a way of managing pixels involved in brain perfusion ROI. This paperpresents the analysis of effectiveness of thresholding techniques in brainperfusion ROI detection on 2T-waited MR images of a human head with abnormalbrain anatomy. Four threshold-based algorithms implementation are considered:according to Otsu method as global thresholding, according to Niblack method aslocal thresholding, thresholding in approximate anatomical brain location, andbrute force thresholding. The analysis is done using comparison of qualitativemaps produced from thresholded images and from the reference ones. Pearsoncorrelation analysis showed strong positive (r was ranged from 0.7123 to0.8518, p<0.01) and weak positive (r<0.35, p<0.01) relationship in case ofconducted experiments with CBF, CBV, MTT and Tmax maps, respectively. Linearregression analysis showed at level of 95% confidence interval that mapsproduced from thresholded images were subject to scale and offset errors in allconducted experiments. The experimental results showed that widely usedthresholding methods are an ineffective way of managing pixels involved inbrain perfusion ROI. Thresholding as brain segmentation tool can lead to poorplacement of perfusion ROI and, as a result, produced maps will be subject toartifacts and can cause falsely high or falsely low perfusion parametersassessment.

Analysis of effectiveness of thresholding in perfusion ROI detection on T2-weighted MR images with abnormal brain anatomy