Maximum Entropy Thresholding Segmentation Research in 3D Images

被引：0
|
作者：
Li Mingdong [1 ]
Peng Ding [1 ]
Xing Ziyang [1 ]
机构：
[1] China W Normal Univ, Nanchong 637002, Sichuan, Peoples R China
来源：
2ND IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER CONTROL (ICACC 2010), VOL. 5 | 2010年
关键词：
Image segmentation; 3D fuzzy maximum entropy;
D O I：
10.1109/ICACC.2010.5486985
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Aiming at the problems such as long executive time, and extremal extreme extremely complex when using image segmentation method to seek Threshold, a novel 3D maximum entropy image segmentation method is proposed, which uses the threshold of 3D image of the global search space, and takes the gray scale value of prixel and the gray scale mean value of region corresponding to 3D maximum entropy value as the threshold for image segmentation. The experimental results show this method has some advantage in aspects of executive time and astringency.
引用
收藏
页码：45 / 48
页数：4
相关论文
共 50 条
  • [41] Application of Tsallis Cross-entropy in Image Thresholding Segmentation
    Lin, Qian-Qian
    Zhang, Ling
    Wu, Tung-Lung
    Mean, Tean-Shine
    Tseng, Hsien-Wei
    SENSORS AND MATERIALS, 2020, 32 (08) : 2687 - 2696
  • [42] A random walk-based segmentation framework for 3D ultrasound images of the prostate
    Ma, Ling
    Guo, Rongrong
    Tian, Zhiqiang
    Fei, Baowei
    MEDICAL PHYSICS, 2017, 44 (10) : 5128 - 5142
  • [43] Segmentation of UAV-based images incorporating 3D point cloud information
    Vetrivel, A.
    Gerke, M.
    Kerle, N.
    Vosselman, G.
    PIA15+HRIGI15 - JOINT ISPRS CONFERENCE, VOL. I, 2015, 40-3 (W2): : 261 - 268
  • [44] One-Shot Weakly-Supervised Segmentation in 3D Medical Images
    Lei, Wenhui
    Su, Qi
    Jiang, Tianyu
    Gu, Ran
    Wang, Na
    Liu, Xinglong
    Wang, Guotai
    Zhang, Xiaofan
    Zhang, Shaoting
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2024, 43 (01) : 175 - 189
  • [45] Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images
    Liew, AWC
    Yan, H
    CURRENT MEDICAL IMAGING, 2006, 2 (01) : 91 - 103
  • [46] Image enhancement and segmentation of fluid-filled structures in 3D ultrasound images
    Chalana, V
    Dudycha, S
    McMorrow, G
    MEDICAL IMAGING 2003: IMAGE PROCESSING, PTS 1-3, 2003, 5032 : 500 - 506
  • [47] Multi-projection deep learning network for segmentation of 3D medical images
    Indraswari, Rarasmaya
    Kurita, Takio
    Arifin, Agus Zainal
    Suciati, Nanik
    Astuti, Eha Renwi
    PATTERN RECOGNITION LETTERS, 2019, 125 : 791 - 797
  • [48] A Hybrid Approach for Automatic Aorta Segmentation in Abdominal 3D CT Scan Images
    Ahmad, Iftikhar
    Rehman, Sami Ur
    Khan, Imran Ullah
    Ali, Arfa
    Rahman, Hussain
    Jan, Sadeeq
    Wadud, Zahid
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2021, 11 (03) : 712 - 719
  • [49] SEGMENTATION OF 3D CARDIAC ULTRASOUND IMAGES USING CORRELATION OF RADIO FREQUENCY DATA
    Nillesen, M. M.
    Lopata, R. G. P.
    Gerrits, I. H.
    Huisman, H. J.
    Thijssen, J. M.
    Kapusta, L.
    de Korte, C. L.
    2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2, 2009, : 522 - +
  • [50] Segmentation of 3D Echocardiographic Images using Deformable Simplex Meshes and Adaptive Filtering
    Nillesen, M. M.
    Lopata, R. G. P.
    Gerrits, I. H.
    Kapusta, L.
    Huisman, H. J.
    Thijssen, J. M.
    de Korte, C. L.
    4TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERING, 2009, 22 (1-3): : 550 - 554
12345