Antognetti P F, Dellepiane S, Serpico S B, Vernazza G
Istituto di Radiologia, Università, Genova.
Radiol Med. 1989 May;77(5):535-9.
This paper deals with digital radiological image processing, with a special emphasis on quantitative measurement extraction aimed at discriminating different lung pathologies. Texture analysis of these images, not easily performed by the human eye, can be carried out by means of fractal techniques, which allow different textures to be characterized by a single parameter--i.e. the fractal dimension. To extract the fractal dimension of an image, the so-called "blanket" method is employed, which allows the average fractal dimension of an image area to be evaluated. The result of this processing is a new image whose points are assigned the average fractal dimension value of the area they belong to. The behaviors of fractal dimension histograms related to various simulations of lung pathologies are notably different, which allows such pathologies to be effectively characterized and differentiated on a quantitative basis. The radiographic images the "blanket" method was applied to are related to simulations of both pathological and healthy tissues. The behaviors of fractal dimension histograms and the average fractal dimension values allow the characterization of the different pathologies both for single tissues and in case of superimposition of a healthy tissue on a pathological one. The promising simulation results have encouraged us to carry out the present experimental investigation in the field of lung neoplasms.
本文探讨数字放射图像处理，特别着重于旨在鉴别不同肺部病变的定量测量提取。这些图像的纹理分析，人眼难以轻易完成，可通过分形技术进行，该技术能以单个参数——即分形维数来表征不同纹理。为提取图像的分形维数，采用了所谓的“覆盖”方法，它可评估图像区域的平均分形维数。此处理的结果是一幅新图像，其各点被赋予它们所属区域的平均分形维数值。与各种肺部病变模拟相关的分形维数直方图的表现显著不同，这使得能够在定量基础上有效地表征和区分此类病变。应用“覆盖”方法的射线图像与病理组织和健康组织的模拟有关。分形维数直方图的表现以及平均分形维数值能够表征单个组织的不同病变，也能表征健康组织与病变组织叠加情况下的病变。这些有前景的模拟结果促使我们在肺部肿瘤领域开展当前的实验研究。
