A multiresolution diffused expectation-maximization algorithm for medical image segmentation.
In this paper a new method for segmenting medical images is presented, the multiresolution diffused expectation-maximization (MDEM) algorithm. The algorithm operates within a multiscale framework, thus taking advantage of the fact that objects/regions to be segmented usually reside at different scales. At each scale segmentation is carried out via the expectation-maximization algorithm, coupled with anisotropic diffusion on classes, in order to account for the spatial dependencies among pixels. This new approach is validated via experiments on a variety of medical images and its performance is compared with more standard methods.
Boccignone
Giuseppe
G
Natural Computation Lab, DIIIE-Universitá di Salerno, via Ponte Don Melillo, 1, 84084 Fisciano (SA), Italy. boccig@unisa.it
Napoletano
Paolo
P
Caggiano
Vittorio
V
Ferraro
Mario
M
D000465:Algorithms; D003198:Computer Simulation; D005901:Glaucoma / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D012871:Skin Diseases / Q000473:pathology; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data