@inbook{6ea6bda3cf674a3494c64c3fe34152c5,

title = "Segmentation of the left ventricle in cardiac MRI using an ELM model",

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.",

keywords = "Extreme learning machine, Image segmentation, Left ventricle, Magnetic resonance imaging",

author = "Y. Luo and B. Yang and L. Xu and L. Hao and J. Liu and Y. Yao and {van de Vosse}, F.N.",

year = "2016",

doi = "10.1007/978-3-319-28397-5_12",

language = "English",

isbn = "978-3-319-28396-8",

series = "Proceedings in Adaptation Learning and Optimization",

publisher = "Springer",

pages = "147--157",

editor = "J. Cao and K. Mao and J. Wu and A. Lendasse",

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

}