Volume 18, Issue 2 (June 2022)                   IJEEE 2022, 18(2): 1-12 | Back to browse issues page

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Fouladifard S, Behnam H, Gifani P, Shojaeifard M. Feasibility Study of Echocardiographic Images Segmentation Based on Sparse Representation. IJEEE 2022; 18 (2) :1-12
URL: http://ijeee.iust.ac.ir/article-1-1993-en.html
Feasibility Study of Echocardiographic Images Segmentation Based on Sparse Representation
S. Fouladifard , H. Behnam , P. Gifani , M. Shojaeifard
Abstract:   (2650 Views)
A semi-automatic method for the segmentation of the Left Ventricle in echocardiography images is presented. The manual segmentation of the left ventricle in all image sequences takes a lot of time. The proposed method is based on sparse representation and the design of overcomplete dictionaries based on prior knowledge of the intensity variation time curves (IVTC). We used the sparse recovery algorithm of orthogonal matching pursuit (OMP) to find the sparse coefficients of the IVTC signals. We obtained the histogram of non-zero sparse coefficients for all images. The binary images from successive frames were constructed via thresholding. In addition, we defined one image representing all the frames, dividing all the points of the heart into three groups. One group involved the points located inside the cavities in all frames. The second group included the points that belonged to the tissue in all frames. Points that in some frames are located inside the cavities and in some other frames are located inside the tissue. The results on 2D echocardiographic images acquired from both healthy and patient subjects showed good agreement with manual tracing and took a short time for the contour, including the whole left ventricle. According to the cardiology specialist, the value of ejection fraction is correctly calculated, and the error percentages were 0.83 and 2.33 for two healthy data samples. The proposed method can be applied to 3D echocardiography images to obtain the left ventricular volume. This approach also can be used for other types of medical images.
Keywords: Dictionary Design , Echocardiography , Intensity Variation Time Curves (IVTC) Signal , Sparse Representation , Temporal Super-Resolution
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  • This paper presents sparse representation and dictionary design in the segmentation of echocardiographic image sequences.
  • The presented method is based on sparse representation and the design of over-complete dictionaries based on prior knowledge of the intensity variation time curves (IVTC).
  • The extrapolations from ejection fraction calculated from the segmentation results are entirely compatible with manual results.
  • The proposed method can be applied in combination with other techniques of echocardiography images diagnosis to obtain highly accurate computation of the parameters.
  • The proposed method can be applied to 3D echocardiography images to obtain the left ventricular volume.
  • Our method also may be used for other imaging modalities, such as MRI or CT.

Type of Study: Research Paper | Subject: Biomedical Signal & Image Processing
Received: 2020/09/21 | Revised: 2024/05/13 | Accepted: 2021/10/18
Video 9. Original second healthy dataset [ZIP 6926 KB]  (236 Download)
Video 10. Surface changes in second healthy-Proposed method [ZIP 5 KB]  (168 Download)
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.