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
相关论文
共 50 条
  • [1] Unsupervised Image Segmentation Based on Expectation-Maximization Algorithm
    Guan, Ji-shi
    Shi, Yao-wu
    Qiu, Jian-wen
    Hou, Yi-min
    2015 INTERNATIONAL CONFERENCE ON APPLIED MECHANICS AND MECHATRONICS ENGINEERING (AMME 2015), 2015, : 506 - 510
  • [2] Image segmentation using multiresolution wavelet analysis and expectation-maximization (EM) algorithm for digital mammography
    Univ of Massachusetts Dartmouth, N. Dartmouth, United States
    Int J Imaging Syst Technol, 5 (491-504):
  • [3] Image segmentation using multiresolution wavelet analysis and expectation-maximization (EM) algorithm for digital mammography
    Chen, CH
    Lee, GG
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 1997, 8 (05) : 491 - 504
  • [4] Validation of image segmentation and expert quality with an expectation-maximization algorithm
    Warfield, SK
    Zou, KH
    Wells, WM
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION-MICCAI 2002, PT 1, 2002, 2488 : 298 - 306
  • [5] A joint shape evolution approach to medical image segmentation using expectation-maximization algorithm
    Farzinfar, Mahshid
    Teoh, Eam Khwang
    Xue, Zhong
    MAGNETIC RESONANCE IMAGING, 2011, 29 (09) : 1255 - 1266
  • [6] An Expectation-Maximization Approach to Joint Curve Evolution for Medical Image Segmentation
    Farzinfar, Mahshid
    Teoh, Eam Khwang
    Xue, Zhong
    MEDICAL IMAGING 2010: IMAGE PROCESSING, 2010, 7623
  • [7] Image restoration with a generalized expectation-maximization algorithm
    Kudo, Hiroyuki
    Saito, Tsuneo
    Systems and Computers in Japan, 1993, 24 (12): : 43 - 53
  • [8] Automatic image segmentation for concealed object detection using the expectation-maximization algorithm
    Lee, Dong-Su
    Yeom, Seokwon
    Son, Jung-Young
    Kim, Shin-Hwan
    OPTICS EXPRESS, 2010, 18 (10): : 10659 - 10667
  • [9] Synergy between Object Recognition and Image Segmentation Using the Expectation-Maximization Algorithm
    Kokkinos, Iasonas
    Maragos, Petros
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2009, 31 (08) : 1486 - 1501
  • [10] Brain image segmentation using a combination of expectation-maximization algorithm and watershed transform
    Kwon, Goo-Rak
    Basukala, Dibash
    Lee, Sang-Woong
    Lee, Kun Ho
    Kang, Moonsoo
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2016, 26 (03) : 225 - 232
12345