-transparent.png){kind=logo}
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.
Related Publications
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
December 2002,
Biometrics,
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
September 2017,
Diagnosis (Berlin, Germany),
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
January 2004,
IEEE transactions on medical imaging,
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
January 1992,
Journal of X-ray science and technology,
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
April 2006,
Physics in medicine and biology,
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
January 2002,
Studies in health technology and informatics,
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
December 1968,
Diseases of the chest,
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
January 1975,
Journal de radiologie, d'electrologie, et de medecine nucleaire,
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
July 2017,
IUCrJ,
L Bocchi, and
G Coppini, and
R De Dominicis, and
G Valli
November 2021,
IEEE transactions on medical imaging,
