A multiresolution diffused expectation-maximization algorithm for medical image segmentation

被引：11
作者：
Boccignone, Giuseppe
Napoletano, Paolo
Caggiano, Vittorio
Ferraro, Mario
机构：
[1] Univ Salerno, DIIIE, Nat Computat Lab, I-84084 Fisciano, SA, Italy
[2] Univ Naples Federico II, Dipartimento Informat & Sistemist, I-80125 Naples, Italy
[3] Univ Turin, Dipartimento Fis Sperimentale, I-10100 Turin, Italy
来源：
COMPUTERS IN BIOLOGY AND MEDICINE | 2007年 / 37卷 / 01期
关键词：
image segmentation; expectation-maximization; multiresolution;
D O I：
10.1016/j.compbiomed.2005.10.002
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码：83 / 96
页数：14
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