### Uittreksel

Originele taal-2 | Engels |
---|---|

Titel | Proceedings of ELM-2015, Vol. 1: Theory, Algorithms and Applications (I) |

Redacteuren | J. Cao, K. Mao, J. Wu, A. Lendasse |

Plaats van productie | Dordrecht |

Uitgeverij | Springer |

Pagina's | 147-157 |

Aantal pagina's | 11 |

ISBN van geprinte versie | 978-3-319-28396-8 |

DOI's | |

Status | Gepubliceerd - 2016 |

### Publicatie series

Naam | Proceedings in Adaptation Learning and Optimization |
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### Citeer dit

*Proceedings of ELM-2015, Vol. 1: Theory, Algorithms and Applications (I)*(blz. 147-157). (Proceedings in Adaptation Learning and Optimization). Dordrecht: Springer. https://doi.org/10.1007/978-3-319-28397-5_12

}

*Proceedings of ELM-2015, Vol. 1: Theory, Algorithms and Applications (I).*Proceedings in Adaptation Learning and Optimization, Springer, Dordrecht, blz. 147-157. https://doi.org/10.1007/978-3-319-28397-5_12

**Segmentation of the left ventricle in cardiac MRI using an ELM model.** / Luo, Y.; Yang, B.; Xu, L.; Hao, L.; Liu, J.; Yao, Y.; van de Vosse, F.N.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Hoofdstuk › Academic › peer review

TY - CHAP

T1 - Segmentation of the left ventricle in cardiac MRI using an ELM model

AU - Luo, Y.

AU - Yang, B.

AU - Xu, L.

AU - Hao, L.

AU - Liu, J.

AU - Yao, Y.

AU - van de Vosse, F.N.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Extreme learning machine

KW - Image segmentation

KW - Left ventricle

KW - Magnetic resonance imaging

U2 - 10.1007/978-3-319-28397-5_12

DO - 10.1007/978-3-319-28397-5_12

M3 - Chapter

SN - 978-3-319-28396-8

T3 - Proceedings in Adaptation Learning and Optimization

SP - 147

EP - 157

BT - Proceedings of ELM-2015, Vol. 1: Theory, Algorithms and Applications (I)

A2 - Cao, J.

A2 - Mao, K.

A2 - Wu, J.

A2 - Lendasse, A.

PB - Springer

CY - Dordrecht

ER -