NDLI: Liver tumor detection and segmentation using kernel-based extreme learning machine

Content Provider	IEEE Xplore Digital Library
Author	Weimin Huang Ning Li Ziping Lin Guang-Bin Huang Weiwei Zong Jiayin Zhou Yuping Duan
Copyright Year	2013
Description	Author affiliation: Inst. for Infocomm Res., Singapore, Singapore (Weimin Huang; Jiayin Zhou; Yuping Duan) \|\| Sch. of EEE, Nanyang Technol. Univ., Singapore, Singapore (Ning Li; Ziping Lin; Guang-Bin Huang; Weiwei Zong)
Abstract	This paper presents an approach to detection and segmentation of liver tumors in 3D computed tomography (CT) images. The automatic detection of tumor can be formulized as novelty detection or two-class classification issue. The method can also be used for tumor segmentation, where each voxel is to be assigned with a correct label, either a tumor class or nontumor class. A voxel is represented with a rich feature vector that distinguishes itself from voxels in different classes. A fast learning algorithm Extreme Learning Machine (ELM) is trained as a voxel classifier. In automatic liver tumor detection, we propose and show that ELM can be trained as a one-class classifier with only healthy liver samples in training. It results in a method of tumor detection based on novelty detection. We compare it with two-class ELM. To extract the boundary of a tumor, we adopt the semi-automatic approach by randomly selecting samples in 3D space within a limited region of interest (ROI) for classifier training. Our approach is validated on a group of patients' CT data and the experiment shows good detection and encouraging segmentation results.
Starting Page	3662
Ending Page	3665
File Size	585389
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610337
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tumors Liver Computed tomography Training Kernel Image segmentation Three-dimensional displays
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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