Skeletal muscle composition, often characterized by the degree of intramuscular adipose tissue, deteriorates with aging and disease and contributes to impairments in function and metabolism. Ultrasound can provide surrogate measures of muscle composition through measurement of echo intensity; however, there are several limitations associated with its analysis. More complex image processing features, broadly known as texture analysis, can also provide surrogates of muscle composition and may circumvent some of the limitations associated with muscle echo intensity. Here, texture features from the intensity histogram, gray-level co-occurrence matrix, run-length matrix, local binary pattern, blob analysis, texture anisotropy index and wavelet analysis are discussed. The purpose of this review was to provide a conceptual understanding of texture analysis as it pertains to muscle composition of ultrasound images.

骨骼肌成分通常以肌肉内脂肪组织的程度为特征，随着衰老和疾病而恶化，并导致功能和新陈代谢受损。超声波可以通过测量回声强度来提供肌肉成分的替代测量值；然而，它的分析存在一些局限性。更复杂的图像处理功能，广泛称为纹理分析，也可以提供肌肉成分的替代物，并可能规避与肌肉回声强度相关的一些限制。在这里，讨论了来自强度直方图的纹理特征、灰度共生矩阵、游程矩阵、局部二进制模式、斑点分析、纹理各向异性指数和小波分析。