Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images

Abstract

Musculoskeletal ultrasound imaging can be used to investigate the skeletal muscle structure in terms of architecture (thickness, cross-sectional area, fascicle length and fascicle pennation angle) and texture. Gray-scale analysis is commonly used to characterize transverse scans of the muscle. Gray mean value is used to distinguish between normal and pathologic muscles, but it depends on the image acquisition system and its settings. In this study, quantitative ultrasonography was performed on five muscles (biceps brachii, vastus lateralis, rectus femoris, medial gastrocnemius and tibialis anterior) of 20 healthy patients (10 women, 10 men) to assess the characterization performance of higher-order texture descriptors to differentiate genders and muscle types. A total of 53 features (7 first-order descriptors, 24 Haralick features, 20 Galloway features and 2 local binary pattern features) were extracted from each muscle region of interest (ROI) and were used to perform the multivariate linear regression analysis (MANOVA). Our results show that first-order descriptors, Haralick features (energy, entropy and correlation measured along different angles) and local binary pattern (LBP) energy and entropy were highly linked to the gender, whereas Haralick entropy and symmetry, Galloway texture descriptors and LBP entropy helped to distinguish muscle types. Hence, the combination of first-order and higher-order texture descriptors (Haralick, Galloway and LBP) can be used to discriminate gender and muscle types. Therefore, multi-texture analysis may be useful to investigate muscle damage and myopathic disorders.

Keywords: Galloway features; Gray-scale mean value; Haralick features; Local binary pattern; MANOVA; Muscle characterization; Muscle ultrasonography; Musculoskeletal ultrasound; Texture analysis.