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.