In 2017 25th European Signal Processing Conference (EUSIPCO), pages 251-255, Aug, 2017. Paper doi abstract bibtex
Automatic segmentation of distinct muscles is a crucial step for quantitative analysis of muscle's tissue properties. Magnetic resonance (MR) imaging provides a superior soft tissue contrast and noninvasive means for assessing muscular characteristics. However, automatic segmentation of muscles using common morphological MR imaging is very challenging as the intensities and textures of adjacent muscles are similar and the boundaries between them are mostly invisible or discontinuous. In this paper, we propose a novel fully automatic framework for 3D segmentation of muscles on water MR images. This framework generates the 3D average and probabilistic atlases of the targeted muscle to automatically define the labeled seeds, the edges weights, and the constraints of a constrained Random Walker algorithm. Also, the low-pass filtered atlas-derived muscle probability map is used to augment the intensities prior to the graph-based segmentation. This enables automatic localization of the targeted muscle and enforces dissimilarities between its intensities and the intensities of adjacent lean tissues. The proposed algorithm outperforms the original random Walker algorithm and the conventional multi-atlas registration for muscle segmentation and is less sensitive to errors in the manually segmented muscle masks used for training (atlas computation).