Robust Muscle Cell Quantification Using Structured Edge Detection and Hierarchical Segmentation

Morphological characteristics of muscle cells, such as cross-sectional areas (CSAs), are critical factors to determine the muscle health. Automatic muscle cell segmentation is often the first prerequisite for quantitative analysis. In this paper, we have proposed a novel muscle cell segmentation algorithm that contains two steps: 1) A structured edge detection algorithm that can capture the inherent edge patterns of muscle images, and 2) a hierarchical segmentation algorithm. A set of nested partitions are first constructed, and a best subset selection algorithm is then designed to choose a maximum weighted subset of non-overlapping partitions as the final segmentation result. We have experimentally demonstrated that the proposed structured edge detection based hierarchical segmentation algorithm outperforms other state of the arts for muscle image segmentation.