title: Correlation modeling for compression of computed tomography images.
abstract: Computed tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3-D images that aid medical diagnosis. Previous approaches for coding such 3-D images propose to employ multicomponent transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this paper, we propose a novel analysis which accurately predicts when the use of a multicomponent transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multicomponent transforms are appropriate for images with correlation coefficient r in excess of 0.87.
authors: Juan
authors: J
authors: Munoz-Gomez
authors: Joan
authors: J
authors: Bartrina-Rapesta
authors: Michael W
authors: MW
authors: Marcellin
authors: Joan
authors: J
authors: Serra-Sagristà
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D014057:Tomography, X-Ray Computed / Q000379:methods*