Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation

Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation
Title:
Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation
Other Titles:
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Keywords:
Publication Date:
26 August 2014
Citation:
W. Huang et al., "Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation," 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, 2014, pp. 4675-4678. doi: 10.1109/EMBC.2014.6944667
Abstract:
This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients’ CT data and experiment show promising results.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1557-170X
ISBN:
978-1-4244-7929-0
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