title: Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation.
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.
authors.forename: Weimin
authors.initials: W
authors.lastname: Huang
authors.forename: Yongzhong
authors.initials: Y
authors.lastname: Yang
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Lin
authors.forename: Guang-Bin
authors.initials: GB
authors.lastname: Huang
authors.forename: Jiayin
authors.initials: J
authors.lastname: Zhou
authors.forename: Yuping
authors.initials: Y
authors.lastname: Duan
authors.forename: Wei
authors.initials: W
authors.lastname: Xiong
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional; D008113:Liver Neoplasms / Q000175:diagnosis*; D011897:Random Allocation; D014057:Tomography, X-Ray Computed / Q000379:methods*