Integrating region and edge information for the automatic segmentation of interventional magnetic resonance images of the shoulder complex

This paper proposes a new 2D segmentation method for MR shoulder images. Due to the significant length of the image sequences, we aim at minimizing the user intervention in the segmentation process. Our method integrates region and edge information in a coherent manner. In fact, the edge information is used in the definition of an adaptive similarity measure for iterative pixel aggregation. The seeds for the region growing process are defined automatically, which is essential for processing long image sequences with variable average brightness. Moreover, the proposed segmentation approach implements parallel region growing processes, and allows for dynamic region merging at successive iterations. To assess the performance of the proposed approach, we followed a standard methodology used for validating 2D segmentation, as well as a quantitative and qualitative evaluation of the 3D shoulder model reconstructed from the segmented image sequences.