ANALYSIS OF EFFECTIVENESS OF THRESHOLDING IN PERFUSION ROI DETECTION ON T2-WEIGHTED MR IMAGES WITH ABNORMAL BRAIN ANATOMY

Background. 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 T2-weighted 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 in modern software applications for perfusion data analysis. Objective. This paper presents the analysis of effectiveness of thresholding techniques in brain perfusion ROI detection on T2-weighted MR images of a human head with abnormal brain anatomy. Methods. 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 result of all algorithms is images with pixels’ values changed to zero for background regions (air pixels and pixels that represent non-brain tissues) and original values for foreground regions (brain perfusion ROIs). The analysis is done using comparison of qualitative perfusion maps produced from thresholded images and from the reference ones (manual brain tissues delineation by experienced radiologists). The same DSC perfusion MR datasets of a human head with abnormal brain anatomy from 12 patients with cerebrovascular disease are used for comparison. Results. 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 perfusion maps, respectively. Linear regression analysis showed at level of 95 % confidence interval that perfusion maps produced from thresholded images were subject to scale and offset errors in all conducted experiments. Conclusions. 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 perfusion maps will be subject to artifacts and can cause falsely high or falsely low perfusion parameter assessment.