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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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/10356/54527 |
| _version_ | 1811684730281656320 |
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| author | Li, Ning. |
| author2 | Lin Zhiping |
| author_facet | Lin Zhiping Li, Ning. |
| author_sort | Li, Ning. |
| collection | NTU |
| 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]. |
| first_indexed | 2024-10-01T04:33:16Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/54527 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:33:16Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | DRNTU::Engineering Li, Ning. Liver tumor detection and segmentation using kernel-based extreme learning machine |
| title | 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_short | Liver tumor detection and segmentation using kernel-based extreme learning machine |
| title_sort | liver tumor detection and segmentation using kernel based extreme learning machine |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/54527 |
| work_keys_str_mv | AT lining livertumordetectionandsegmentationusingkernelbasedextremelearningmachine |