title: Data predictability for compression of digital fluorography images.
abstract: Images obtained by digital fluorography were checked for compressability. These images include images of coronary vessels and images of peripheral vessels. These images have a very low signal-to-noise ratio compared to the optical images usually used for developing compression methods. Configurational entropy was used to represent the information content of these images. Reversible prediction algorithms were extensively checked in a search for minimal residual information, enabling more efficient reversible compression. Optimal results were obtained for algorithms based on two or three neighboring pixels and a semiempirical rule, based on the noise level, was found which decides on the best approach. It was found that raw data images are more predictable than subtracted images although the latter are visually preferred.
authors.affiliation: Elscint, Ltd., Haifa, Israel.
authors.forename: R
authors.initials: R
authors.lastname: Niv
authors.forename: Y
authors.initials: Y
authors.lastname: Shimoni
mesh_terms: D000465:Algorithms; D006801:Humans; D008962:Models, Theoretical; D010779:Photofluorography / Q000379:methods*; D014965:X-Rays