Segmentation of the left ventricle in cardiac MRI using an ELM model

Y. Luo, B. Yang, L. Xu, L. Hao, J. Liu, Y. Yao, F.N. van de Vosse

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

10 Citations (Scopus)

Abstract

In this paper, an automatic left ventricle (LV) segmentation method based on an Extreme Learning Machine (ELM) is presented. Firstly, according to background and foreground, all sample pixels of Magnetic Resonance Imaging (MRI) images are divided into two types, and then 23-dimensional features of each pixel are extracted to generate a feature matrix. Secondly, the feature matrix is input into the ELM to train the ELM. Finally, the LV is segmented by the trained ELM. Experimental results show that the mean speed of LV segmentation based on the ELM is about 25 times faster than that of the level set, about 7 times faster than that of the SVM. The mean values of mad and maxd of image segmentation based on the ELM is about 80 and 83.1 % of that of the level set and the SVM, respectively. The mean value of dice of image segmentation based on the ELM is about 8 and 2 % higher than that of the level set and the SVM, respectively. The standard deviation of the proposed method is the lowest among all three methods. The results prove that the proposed method is efficient and satisfactory for the LV segmentation.
Original languageEnglish
Title of host publicationProceedings of ELM-2015, Vol. 1: Theory, Algorithms and Applications (I)
EditorsJ. Cao, K. Mao, J. Wu, A. Lendasse
Place of PublicationDordrecht
PublisherSpringer
Pages147-157
Number of pages11
ISBN (Print)978-3-319-28396-8
DOIs
Publication statusPublished - 2016

Publication series

NameProceedings in Adaptation Learning and Optimization

Keywords

  • Extreme learning machine
  • Image segmentation
  • Left ventricle
  • Magnetic resonance imaging

Cite this

Luo, Y., Yang, B., Xu, L., Hao, L., Liu, J., Yao, Y., & van de Vosse, F. N. (2016). Segmentation of the left ventricle in cardiac MRI using an ELM model. In J. Cao, K. Mao, J. Wu, & A. Lendasse (Eds.), Proceedings of ELM-2015, Vol. 1: Theory, Algorithms and Applications (I) (pp. 147-157). (Proceedings in Adaptation Learning and Optimization). Dordrecht: Springer. https://doi.org/10.1007/978-3-319-28397-5_12