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

In this project, a semi-automatic approach of the detection and segmentation of liver tumors from 3D computed tomography (CT) images is presented. The automatic detection of liver tumor can be formulized as a novelty detection or two-class classification issue. The method can also be used for tumor...

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Main Author: Li, Ning.
Other Authors: Lin Zhiping
Format: Final Year Project
Language:English
Published: 2013
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Online Access:http://hdl.handle.net/10356/54527
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-545272023-07-07T17:28:26Z Liver tumor detection and segmentation using kernel-based extreme learning machine Li, Ning. Lin Zhiping School of Electrical and Electronic Engineering A*STAR Institute for Infocomm Research DRNTU::Engineering In this project, a semi-automatic approach of the detection and segmentation of liver tumors from 3D computed tomography (CT) images is presented. The automatic detection of liver tumor can be formulized as a 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 a non-tumor 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 the training dataset. It results in a method of tumor detection based on novelty detection. Then we compare it with the two-class ELM detection case. 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. Part of the work presented in this FYP report was accepted as a conference paper [23] to be presented at the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13) to be held in Osaka International Convention Center, in Osaka, Japan on July 3-7, 2013 [22]. Bachelor of Engineering 2013-06-21T06:48:02Z 2013-06-21T06:48:02Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/54527 en Nanyang Technological University 55 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Li, Ning.
Liver tumor detection and segmentation using kernel-based extreme learning machine
description In this project, a semi-automatic approach of the detection and segmentation of liver tumors from 3D computed tomography (CT) images is presented. The automatic detection of liver tumor can be formulized as a 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 a non-tumor 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 the training dataset. It results in a method of tumor detection based on novelty detection. Then we compare it with the two-class ELM detection case. 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. Part of the work presented in this FYP report was accepted as a conference paper [23] to be presented at the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13) to be held in Osaka International Convention Center, in Osaka, Japan on July 3-7, 2013 [22].
author2 Lin Zhiping
author_facet Lin Zhiping
Li, Ning.
format Final Year Project
author Li, Ning.
author_sort Li, Ning.
title Liver tumor detection and segmentation using kernel-based extreme learning machine
title_short Liver tumor detection and segmentation using kernel-based extreme learning machine
title_full Liver tumor detection and segmentation using kernel-based extreme learning machine
title_fullStr Liver tumor detection and segmentation using kernel-based extreme learning machine
title_full_unstemmed Liver tumor detection and segmentation using kernel-based extreme learning machine
title_sort liver tumor detection and segmentation using kernel-based extreme learning machine
publishDate 2013
url http://hdl.handle.net/10356/54527
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