title: Tissue characterization from X-ray images.
abstract: The study of the fine-scale structure of biological tissues is crucial for diagnosing a wide number of different diseases. In X-ray images, fine structures usually induce a correlation among image gray levels and are commonly perceived as textures. In this paper, we report on a Computer Vision approach to the characterization of biological tissues as imaged by standard X-ray techniques. In particular, using features derived from co-occurrence matrices, we have assessed spatial gray-level dependence of bone tissue and lung parenchyma images. A hybrid neural network was adopted to distinguish pathological tissues from normal ones and to classify different pathologies.
authors.affiliation: Department of Electronic Engineering, University of Florence, Italy.
authors.forename: L
authors.initials: L
authors.lastname: Bocchi
authors.forename: G
authors.initials: G
authors.lastname: Coppini
authors.forename: R
authors.initials: R
authors.lastname: De Dominicis
authors.forename: G
authors.initials: G
authors.lastname: Valli
mesh_terms: D001847:Bone Diseases / Q000000981:diagnostic imaging; D001842:Bone and Bones / Q000000981:diagnostic imaging; D003937:Diagnosis, Differential; D006801:Humans; D007719:Knee Joint / Q000000981:diagnostic imaging; D008168:Lung / Q000000981:diagnostic imaging; D008171:Lung Diseases / Q000000981:diagnostic imaging; D016571:Neural Networks, Computer; D016011:Normal Distribution; D011336:Probability; D011856:Radiographic Image Enhancement / Q000379:methods*; D012680:Sensitivity and Specificity