Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada.
Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada.
Ultrasound Med Biol. 2021 Apr;47(4):880-895. doi: 10.1016/j.ultrasmedbio.2020.12.012. Epub 2021 Jan 13.
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.
骨骼肌组成,通常以肌肉内脂肪组织的程度为特征,随着年龄的增长和疾病的发生而恶化,并导致功能和代谢受损。超声可以通过测量回声强度提供肌肉组成的替代指标;然而,其分析存在几个局限性。更复杂的图像处理特征,通常称为纹理分析,也可以提供肌肉组成的替代指标,并且可以规避与肌肉回声强度相关的一些限制。在这里,讨论了强度直方图、灰度共生矩阵、行程长度矩阵、局部二值模式、斑点分析、纹理各向异性指数和小波分析的纹理特征。本综述的目的是提供对与超声图像肌肉组成相关的纹理分析的概念性理解。