A robust thresholding method with applications to brain MR image segmentation

This paper brings forth two main novel aspects: 1) a generic thresholding method that is robust to degradation in the image contrast; hence, quality and 2) a new knowledge-based segmentation framework for brain MR images that first utilizes a clustering algorithm, and then the proposed thresholding method. The new thresholding method accurately computes a threshold value even for images with very low visual quality having very close class means. It also consistently outperforms known thresholding methods. The segmentation algorithm, on the other hand, generates almost constant segmentation performance in a wide range of scan parameter values. It utilizes first a clustering algorithm to identify the CSF (cerebrospinal fluid) region and then focuses on white matter (WM) - gray matter (GM) separation by using the novel thresholding method. We show the robustness of the proposed algorithms with a simulated dataset obtained with various parameter values and a real dataset of brain MR dual-echo sequences of patients with possible iron accumulation.