Nodera Hiroyuki, Sogawa Kazuki, Takamatsu Naoko, Mori Atsuko, Yamazaki Hiroki, Izumi Yuishin, Kaji Ryuji
Department of Neurology, Tokushima University.
Tokushima University, Faculty of Medicine.
J Med Invest. 2018;65(3.4):274-279. doi: 10.2152/jmi.65.274.
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.
纹理分析通过图像的纹理内容来表征图像中的区域，并已被用于推断诸如骨骼肌等医学图像的潜在结构。尽管纹理分析在神经肌肉疾病的组织诊断和评估疾病进展方面可能有用，但由于缺乏诸如衰老影响等信息，其在这些目的中的应用受到限制。因此，我们对20多岁到80多岁的健康个体的腓肠肌内侧进行了纹理分析。在6类283个纹理特征中，与直方图、共生矩阵、绝对梯度和小波相关的特征在17%-40%的参数中与年龄相关，而与行程长度矩阵和自回归模型相关的特征均与年龄无显著相关性。本研究表明，许多纹理特征存在年龄依赖性，在阐明临床意义时需要考虑这一点。相比之下，与行程长度矩阵和自回归模型相关的特征可能具有临床应用价值。《医学调查杂志》65:274 - 279，2018年8月。
