Texture analysis characterizes regions in an image by their texture content and has been utilized to infer the underlying structures of medical images such as skeletal muscles. Although potentially useful in tissue diagnosis and assessing disease progression of neuromuscular diseases, the use of texture analysis in such purposes are limited, due to lack of information such as effects of aging. Thus, we performed texture analysis of medial gastrocnemius in healthy individuals form their 20s to late 80s. Among the 283 texture features in 6 classes, the features related to histogram, co-occurrence matrix, absolute gradient, and wavelet were correlated to age in 17-40% of the parameters, while none of the features related to run-length matrix and autoregressive model had significant correlation to age. This study showed that age-dependency in many texture features are present and need to be taken into account in elucidating the clinical significance. By contrast, the features related to run-length matrix and autoregressive model could have clinical utility. J. Med. Invest. 65:274-279, August, 2018.
Keywords: autoregressive model; run‐length matrix; skeletal muscle; texture; ultrasound.