An electrostatic deformable model for medical image segmentation

被引:18
|
作者
Chang, Herng-Hua [1 ,2 ]
Valentino, Daniel J. [1 ,2 ,3 ]
机构
[1] Univ Calif Los Angeles, Biomed Engn IDP, Los Angeles, CA 90095 USA
[2] Univ Calif Los Angeles, Lab Neuro Imaging LONI, Los Angeles, CA 90095 USA
[3] Univ Calif Los Angeles, DINR, Los Angeles, CA 90095 USA
关键词
segmentation; deformable models; level sets; charged fluid model (CFM); Poisson's equation; electrostatic equilibrium; CT; MRI; 3DRA;
D O I
10.1016/j.compmedimag.2007.08.012
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
A new deformable model, the charged fluid model (CFM), that uses the simulation of charged elements was used to segment medical images. Poisson's equation was used to guide the evolution of the CFM in two steps. In the first step, the elements of the charged fluid were distributed along the propagating interface until electrostatic equilibrium was achieved. In the second step, the propagating front of the charged fluid was deformed in response to the image gradient. The CFM provided sub-pixel precision, required only one parameter setting, and required no prior knowledge of the anatomy of the segmented object. The characteristics of the CFM were compared to existing deformable models using CT and MR images. The results indicate that the CFM is a promising approach for the segmentation of anatomic structures in a wide variety of medical imaaes across different modalities. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:22 / 35
页数:14
相关论文
共 50 条
  • [31] A software framework for preprocessing and level set segmentation of medical image data
    Fritscher, KD
    Schubert, R
    MEDICAL IMAGING 2005: IMAGE PROCESSING, PT 1-3, 2005, 5747 : 1742 - 1752
  • [32] Active Volume Models for Medical Image Segmentation
    Shen, Tian
    Li, Hongsheng
    Huang, Xiaolei
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2011, 30 (03) : 774 - 791
  • [33] Medical Image Segmentation Using Genetic Algorithms
    Maulik, Ujjwal
    IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2009, 13 (02): : 166 - 173
  • [34] Medical endoscopic image segmentation using snakes
    Yoon, SW
    Lee, HK
    Kim, JH
    Lee, MH
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2004, E87D (03): : 785 - 789
  • [35] Generative Deep Belief Model for Improved Medical Image Segmentation
    Balaji, Prasanalakshmi
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2023, 35 (01): : 1 - 14
  • [36] SAMU: An Efficient and Promptable Foundation Model for Medical Image Segmentation
    Bae, Joseph
    Guo, Xueqi
    Yerebakan, Halid
    Shinagawa, Yoshihisa
    Farhand, Sepehr
    FOUNDATION MODELS FOR GENERAL MEDICAL AI, MEDAGI 2024, 2025, 15184 : 134 - 142
  • [37] A Novel Medical Image Segmentation Method Based on GCBAC Model
    Li, Liangliang
    Si, Yujuan
    Ma, Baoluo
    Jia, Zhenhong
    2017 10TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL. 1, 2017, : 161 - 164
  • [38] DHT: Deformable Hybrid Transformer for Aerial Image Segmentation
    Zhang, Yan
    Gao, Xiyuan
    Duan, Qingyan
    Yuan, Lin
    Gao, Xinbo
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [39] Improving specificity to image features for segmentation with deformable models
    von Berg, J
    Pekar, V
    Truyen, R
    Lobregt, S
    Kaus, MR
    MEDICAL IMAGING 2003: IMAGE PROCESSING, PTS 1-3, 2003, 5032 : 1233 - 1242
  • [40] Watermarked cardiac CT image segmentation using deformable models and the Hermite transform
    Gomez-Coronel, Sandra L.
    Moya-Albor, Ernesto
    Escalante-Ramirez, Boris
    Brieva, Jorge
    10TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, 2015, 9287