Analysis of effectiveness of thresholding in perfusion ROI detection on T2-weighted MR images with abnormal brain anatomy
This addresses the problem of inaccurate brain perfusion ROI detection in medical imaging for patients with abnormal brain anatomy, which is incremental as it evaluates existing thresholding methods rather than proposing a new solution.
The paper analyzed the effectiveness of thresholding techniques for detecting brain perfusion regions of interest (ROI) on T2-weighted MR images with abnormal anatomy, finding that widely used methods are ineffective and lead to artifacts and errors in perfusion parameter assessment, with Pearson correlations ranging from 0.7123 to 0.8518 for some maps but weak correlations for others.
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.