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A novel machine-learning algorithm to estimate the position and size of myocardial infarction for MRI sequence

  • Chenchu Xu
  • Lin Xu
  • Heye ZhangEmail author
  • Yanping Zhang
  • Xiuquan Du
  • Shuo Li
Article
  • 24 Downloads

Abstract

Early and accurate assessment of myocardial abnormalities is of utmost importance in the diagnosis of myocardial infarction (MI). In this study, we proposed a machine-learning and image motion statistic based approach to automating the detection and localization of MI area in magnetic resonance images. Unlike the existing techniques, the proposed method that could be directly acquired position and size of MI area with sub-pixel precision. Standard clinical magnetic resonance image and delayed enhancement imaging data of 58 patients with MI were used for developing this algorithm. First, we are located and extracted the LV from the original MR image. Then, we using a novel Optical Flow algorithm to building statistical image motion features include the motion trajectories of each Point on myocardium in whole frames. In the end, we using these results as inputs to a support vector machine classifier, which can obtain an MI area assessment of the myocardium. Compared to the pixel by pixel in delayed enhancement imaging, the proposed algorithm yielded the highest classification accuracy of 93.34% and the kappa measure of 0.74. The field experiments our method performs significantly better than other recent methods, and can lead to a promising diagnostic support tool to assists clinicians, particularly for novice readers with limited experience.

Keywords

Myocardial infarction Optical flow Motion feature 

Mathematics Subject Classification

68T10 

Notes

Acknowledgements

Funding was provided by National Key Research and Development Program of China (Grant No. 2016YFC1300300), The Natural Science Foundation of Jiangsu Province of China (Grant BK20150931). National Natural Science Foundation of China (Grant Nos. 61771464, U1801265, 61702275, 41775008, 61673020), Shen Zhen Research and Innovation Funding (Grant No. JCYJ20170307165309009), Provincial Natural Science Research Program of the Higher Education Institutions of Anhui Province (Grant No. KJ2016A016).

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenZhenChina
  2. 2.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  3. 3.Department of CardiologyGuangzhou General Hospital of Guangzhou Military Region, PLAGuangzhouChina
  4. 4.Department of Medical ImagingWestern UniversityLondonCanada

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