The Application of Texture Feature Analysis of Rectus Femoris Based on Local Binary Pattern (LBP) Combined With Gray‐Level Co‐Occurrence Matrix (GLCM) in Sarcopenia

Abstract

Objectives-In order to detect the changes in muscle texture of sarcopenia and to explore a new method of ultrasound assessment of muscle changes in sarcopenia.Methods-we used the local binary pattern (LBP) combined with gray-level co-occurrence matrix (GLCM) method to extract and quantitatively analyze the texture information of the rectus femoris of different people, and initially verified the robustness of this method to image gain changes. We recruited young volunteers, elderly volunteers without sarcopenia, and elderly volunteers with sarcopenia in this cross-sectional study. We scanned the rectus femoris and extracted their muscle texture features.Results-We found that when ultrasonographic gain varied from 40% to 70%, the intraclass correlation coefficient (ICC) of contrast, entropy, and homogeneity were 0.989, 0.973, and 0.989, respectively. Body mass index was significantly related to contrast (r = 0.285, P < .05), and age had a significant correlation with contrast and homogeneity (r = À0.259 and r = 0.269, P < .05). The elderly volunteers with sarcopenia had the highest entropy (0.363 [0.342-0.403]) and homogeneity (2.203 [2.162-2.277]) in the texture of the rectus femoris among the three groups, and at the same time had the lowest contrast (44.583 [43.492-47.399]), and all P < .05.Conclusion-LBP combined with GLCM can be a stable method for extracting muscle texture features. At the same time, the contrast, entropy, and homogeneity of the rectus femoris of the elderly with sarcopenia were significantly different from those of the young volunteers and the elderly without sarcopenia, suggesting the texture features of rectus femoris are potential parameters for evaluating muscle function and pathological changes. Key Words-sarcopenia; texture analysis; ultrasound S arcopenia is defined as an age-related syndrome with decreased skeletal muscle function and loss of muscle mass. 1,2 The concept of the disease was first proposed by Rosenberg et al in 1989, 3 and has been continuously studied worldwide in the past 30 years. For the elderly population, the incidence of sarcopenia is as high as 11.5-40.5%. 4,5 With the gradual aging of the population structure, sarcopenia has become a public health problem that has received widespread attention

Improvements to a GLCM‐based machine‐learning approach for quantifying posterior capsule opacification

“…Among texture analysis methods, the GLCM is commonly employed, providing quantitative descriptions of texture through parameters like entropy and contrast. Nevertheless, the GLCM can be affected by image brightness, requiring high‐quality raw images for accurate analysis 27 . Moreover, the GLCM tends to extract features uniformly across the entire image, potentially compromising its performance by confusing artifacts with lesions.…”

“…Nevertheless, the GLCM can be affected by image brightness, requiring high‐quality raw images for accurate analysis. 27 Moreover, the GLCM tends to extract features uniformly across the entire image, potentially compromising its performance by confusing artifacts with lesions. Extracted GLCM‐features on whole images can be rough, implying feature extraction on a local scale.…”

“…The GLCM has been commonly used for texture analysis in medical images 27 . Given that lesions present in eye images possess specific texture information, the GLCM is employed to extract lesion features.…”

“…The GLCM has been commonly used for texture analysis in medical images. 27 Given that lesions present in eye images possess specific texture information, the GLCM is employed to extract lesion features. It is defined as the distribution of co‐occurring pixel values at a specified offset within an image.…”

Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis

“…Gray-level co-occurrence matrix combines spatial location distribution and brightness distribution characteristics. Compared with other feature extraction algorithms, gray-level co-occurrence matrix can extract more brightness information [ 42 ]. In this work, gray-level co-occurrence matrix was adopted to extract ASM, contrast, and HOM features in muscle USBI images.…”