Abstract
This research work proposes an automatic segmentation approach of the left ventricle (LV) from cardiac magnetic resonance image (CMRI). Although the parametric active contour model (PACM) can be used to segment the LV of CMRI image, it requires the initial contour. This is a manual and time-consuming approach and the accuracy is subjective. The main contribution of this proposal is to introduce an artificial neural network based regression model to predict the initial contour to detect the LV area of a CMRI image. With the automatic predicted contour points by the proposed method, a number of CMRI images are segmented by PACM to get the LV area. The results demonstrate that the proposed automatic segmentation procedure can segment the LV area with negligible deviation with less required time. In addition, this research work also customizes the finely fitted parametric values of the PACM for the CMRI images.
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Al Noman, M., Aowlad Hossain, A.B.M., Asadur Rahman, M. (2020). Initial Point Prediction Based Parametric Active Contour Model for Left Ventricle Segmentation of CMRI Images. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_45
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DOI: https://doi.org/10.1007/978-981-13-7564-4_45
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