Segmentation of the Ventricle Membranes in Short-Axis Sequences by Optical Flow Base on DLSRE Model

被引:1
|
作者
Li Lin [1 ]
Wu Hengfei [1 ]
Li Junhua [2 ]
机构
[1] Bozhou Univ, Dept Elect & Informat Engn, Bozhou 236800, Peoples R China
[2] Nanchang Hongkong Univ, Key Lab Jiangxi Prov Image Proc & Pattern Recogni, Nanchang 330063, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Double‐ level set region evolution; Optical flow; Left ventricle; Image segmentation; LEVEL SET; IMAGE;
D O I
10.1049/cje.2021.03.009
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution (DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.
引用
收藏
页码:460 / 470
页数:11
相关论文
共 50 条
  • [31] Left Ventricle Segmentation Using Graph Searching on Intensity and Gradient and A Priori Knowledge (lvGIGA) for Short-Axis Cardiac Magnetic Resonance Imaging
    Lee, Hae-Yeoun
    Codella, Noel
    Cham, Matthew
    Prince, Martin
    Weinsaft, Jonathan
    Wang, Yi
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2008, 28 (06) : 1393 - 1401
  • [32] MDCT determination of volume and function of the left ventricle: Are short-axis image reformations necessary?
    Juergens, Kai U.
    Seifarth, Harald
    Maintz, David
    Grude, Matthias
    Ozgun, Murat
    Wichter, Thomas
    Heindel, Walter
    Fischbach, Roman
    AMERICAN JOURNAL OF ROENTGENOLOGY, 2006, 186 (06) : S371 - S378
  • [33] Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data
    Xue, Wufeng
    Li, Jiahui
    Hu, Zhiqiang
    Kerfoot, Eric
    Clough, James
    Oksuz, Ilkay
    Xu, Hao
    Grau, Vicente
    Guo, Fumin
    Ng, Matthew
    Li, Xiang
    Li, Quanzheng
    Liu, Lihong
    Ma, Jin
    Grinias, Elias
    Tziritas, Georgios
    Yan, Wenjun
    Atehortua, Angelica
    Garreau, Mireille
    Jang, Yeonggul
    Debus, Alejandro
    Ferrante, Enzo
    Yang, Guanyu
    Hua, Tiancong
    Li, Shuo
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2021, 25 (09) : 3541 - 3553
  • [34] Slice-Level-Guided Convolutional Neural Networks to study the Right Ventricular Segmentation using MRI Short-Axis sequences
    Ammari, Asma
    Mahmoudi, Ramzi
    Hmida, Badii
    Saouli, Rachida
    Bedoui, Mohamed Hedi
    2021 IEEE/ACS 18TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 2021,
  • [35] Segmentation and Registration Coupling from Short-Axis Cine MRI: Application to Infarct Diagnosis
    Marchesseau, Stephanie
    Duchateau, Nicolas
    Delingette, Herve
    STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART: IMAGING AND MODELLING CHALLENGES, 2016, 2017, 10124 : 48 - 56
  • [36] AUTOMATIC ANALYSIS OF LEFT VENTRICLE WALL THICKNESS USING SHORT-AXIS CINE CMR IMAGES
    Khalifa, F.
    Beache, G. M.
    Nitzken, M.
    Gimel'farb, G.
    Giridharan, G.
    El-Baz, A.
    2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, 2011, : 1306 - 1309
  • [37] Automatic Left Ventricle Segmentation from Short-Axis MRI Images Using U-Net with Study of the Papillary Muscles' Removal Effect
    Baccouch, Wafa
    Oueslati, Sameh
    Solaiman, Basel
    Lahidheb, Dhaker
    Labidi, Salam
    JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING, 2023, 43 (03) : 278 - 290
  • [38] Automatic Left Ventricle Segmentation from Short-Axis MRI Images Using U-Net with Study of the Papillary Muscles’ Removal Effect
    Wafa Baccouch
    Sameh Oueslati
    Basel Solaiman
    Dhaker Lahidheb
    Salam Labidi
    Journal of Medical and Biological Engineering, 2023, 43 : 278 - 290
  • [39] A review of approaches investigated for right ventricular segmentation using short-axis cardiac MRI
    Ammari, Asma
    Mahmoudi, Ramzi
    Hmida, Badii
    Saouli, Rachida
    Bedoui, Mohamed Hedi
    IET IMAGE PROCESSING, 2021, 15 (09) : 1845 - 1868
  • [40] Difference between short-axis and long-axis motions of left ventricle by doppler tissue imaging in arterial hypertension
    Galderisi, M
    Caso, P
    Severino, S
    Cicala, S
    Petrocelli, A
    Mininni, N
    de Divitiis, O
    JOURNAL OF HYPERTENSION, 2000, 18 : S36 - S36