Segmentation of Skeletal Muscle in Thigh Dixon MRI Based on Texture Analysis

TL;DR

This paper introduces an automated MRI muscle segmentation method using texture analysis and AdaBoost classification, complemented by an atlas-based approach for individual muscle delineation, aiming to improve efficiency and generalization.

Contribution

It presents a novel automated segmentation framework combining texture features and AdaBoost, along with an atlas-based method for detailed muscle segmentation in MRI.

Findings

Texture analysis improves segmentation accuracy.

Atlas-based approach enhances individual muscle delineation.

Method shows potential for generalized MRI muscle segmentation.

Abstract

Segmentation of skeletal muscles in Magnetic Resonance Images (MRI) is essential for the study of muscle physiology and diagnosis of muscular pathologies. However, manual segmentation of large MRI volumes is a time-consuming task. The state-of-the-art on algorithms for muscle segmentation in MRI is still not very extensive and is somewhat database-dependent. In this paper, an automated segmentation method based on AdaBoost classification of local texture features is presented. The texture descriptor consists of the Histogram of Oriented Gradients (HOG), Wavelet-based features, and a set of statistical measures computed from both the original and the Laplacian of Gaussian filtering of the grayscale MRI. The classifier performance suggests that texture analysis may be a helpful tool for designing a generalized and automated MRI muscle segmentation framework. Furthermore, an atlas-based approach to individual muscle segmentation is also described in this paper. The atlas is obtained by overlaying the muscle segmentation ground truth, provided by a radiologist, after image alignment using an appropriate affine transformation. Then, it is used to define the muscle labels upon the AdaBoost binary segmentation. The developed atlas method provides reasonable results when an accurate muscle tissue segmentation was obtained.