A New Hybrid Brain MR Image Segmentation Algorithm With Super-Resolution, Spatial Constraint-Based Clustering and Fine Tuning

Tissue segmentation from a single brain MR image is of paramount importance for brain reconstruction and analysis. In this paper, we propose a new hybrid algorithm for brain MR image segmentation, combining super-resolution, spatial constraint based clustering and fine-tuning. To smooth noise and improve image clarity, we first amplify the brain MR image by using a super-resolution algorithm - cubic surface fitting with edges in the image as constraints. Then an improved fuzzy c-means clustering algorithm is performed on the amplified image for the global segmentation, in which a shape parameter and an anomaly detection parameter are introduced. With the introduction of these two parameters, the robustness of the clustering is enhanced, and the trade-off between noise smoothing and detail preservation can be controlled more accurately. Furthermore, the local regions around boundaries of different brain tissues (e.g., gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF)) are re-segmented in a fine-tuning process, and a soft voting strategy is adopted for adjusting the incorrect pixels, which makes full use of the boundary details of different tissues. Experimental results show the new algorithm can preserve major brain tissue structures and smooth out noise.