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Vol.9, No.4, November 2020. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Medical Image Segmentation based on Fully Convolutional Network and Minimizing Energy Between Curves
Vo Thi Hong Tuyet, Nguyen Thanh Binh
© 2020 Vo Thi Hong Tuyet, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 9, Issue 4, Pages 1348-1356, ISSN 2217-8309, DOI: 10.18421/TEM94-05, November 2020.
Received: 01 July 2020. Revised: 04 September 2020.
Abstract:
Energy between curves of image has useful for object contour. The edge map is an important task for recognition. The shape that is found by linking between edges will clearly present the useful information of objects. The aim of medical image segmentation is the representation of a medical image into small pieces. In this process, feature extraction must adapt with edge map completely. This paper proposed a solution for medical image segmentation based on fully convolutional network with gradient vector flow snake in bandelet domain. Our approach depends on decomposition in bandelet domain and reconstruction in contour detection by fully convolutional network combining with gradient vector flow snake. To improve the accuracy of the feature's extraction processing, the proposed method detected the edge map in bandelet domain by using fully convolutional network. And its reconstructed objects contour by using gradient vector flow snake combined with the boundary condition. The results of the proposed method have the segmentation clearly with small details of medical images in high-quality and low-quality cases.
Keywords –medical image segmentation, fully convolutional network, bandelet transform, gradient vector flow snake. |
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