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Quantitative Assessment of Heart Function: A Hybrid Mechanism for Left Ventricle Segmentation from Cine MRI Sequences

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Artificial Life and Computational Intelligence (ACALCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10142))

Abstract

In this paper, we propose a hybrid approach for segmenting the left ventricle out of magnetic resonance sequences and apply results of the segmentation for heart quantification. The hybrid approach uses a thresholding-based region growing algorithm coupled with gradient vector flow (GVF). Results of the segmentation steps were used for the quantification process and yielded values of 175.4 ± 51.52 (ml), 66 ± 38.97 (ml), and 61.60 ± 12.79 (%) for end diastolic volume (EDV), end systolic volume (ESV), and ejection fraction (EF), respectively.

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Acknowledgments

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20162220100050); in part by The Leading Human Resource Training Program of Regional Neo Industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (NRF-2016H1D5A1910564); and in part by the Business for Cooperative R&D between Industry, Academy, and Research Institute funding for the Korea Small and Medium Business Administration in 2016 (Grant No. C0395147, S2381631).

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Authors and Affiliations

  1. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

    Muhammad Sohaib & Jong-Myon Kim

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  1. Muhammad Sohaib
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  2. Jong-Myon Kim
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Correspondence to Jong-Myon Kim .

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Editors and Affiliations

  1. The University of Adelaide, Adelaide, South Australia, Australia

    Markus Wagner

  2. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  3. Swinburne University, Melbourne, Victoria, Australia

    Tim Hendtlass

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Sohaib, M., Kim, JM. (2017). Quantitative Assessment of Heart Function: A Hybrid Mechanism for Left Ventricle Segmentation from Cine MRI Sequences. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-51691-2_15

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