Segmentation of liver tumor from CT images
This final year project aims to study characteristics of cancerous and healthy tissues in liver, which are later used by two algorithms, Support Vector Machine and Graph-cut, to do the liver tumor segmentation. Results from two algorithms are compared to evaluate on their efficiency. Besides, anisot...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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2013
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| Online Access: | http://hdl.handle.net/10356/52995 |
| _version_ | 1824453487539257344 |
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| author | La, Cam Chung |
| author2 | Chan Kap Luk |
| author_facet | Chan Kap Luk La, Cam Chung |
| author_sort | La, Cam Chung |
| collection | NTU |
| description | This final year project aims to study characteristics of cancerous and healthy tissues in liver, which are later used by two algorithms, Support Vector Machine and Graph-cut, to do the liver tumor segmentation. Results from two algorithms are compared to evaluate on their efficiency. Besides, anisotropic diffusion filter concept is also introduced as a pre-processing step, so that the final segmentation from both algorithms can be enhanced. Finally, a new semi-automatic method is proposed to improve liver tumor segmentation by finding out the ribcage curves on every CT slice, using the rib bone traces. One can use the rib curve as a post-processing step to fine tune the segmentation. |
| first_indexed | 2025-02-19T03:07:12Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/52995 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:07:12Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/529952023-07-07T16:39:09Z Segmentation of liver tumor from CT images La, Cam Chung Chan Kap Luk School of Electrical and Electronic Engineering A*STAR Institute for Infocomm Research DRNTU::Engineering::Bioengineering This final year project aims to study characteristics of cancerous and healthy tissues in liver, which are later used by two algorithms, Support Vector Machine and Graph-cut, to do the liver tumor segmentation. Results from two algorithms are compared to evaluate on their efficiency. Besides, anisotropic diffusion filter concept is also introduced as a pre-processing step, so that the final segmentation from both algorithms can be enhanced. Finally, a new semi-automatic method is proposed to improve liver tumor segmentation by finding out the ribcage curves on every CT slice, using the rib bone traces. One can use the rib curve as a post-processing step to fine tune the segmentation. Bachelor of Engineering 2013-05-29T06:57:11Z 2013-05-29T06:57:11Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/52995 en Nanyang Technological University 66 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Bioengineering La, Cam Chung Segmentation of liver tumor from CT images |
| title | Segmentation of liver tumor from CT images |
| title_full | Segmentation of liver tumor from CT images |
| title_fullStr | Segmentation of liver tumor from CT images |
| title_full_unstemmed | Segmentation of liver tumor from CT images |
| title_short | Segmentation of liver tumor from CT images |
| title_sort | segmentation of liver tumor from ct images |
| topic | DRNTU::Engineering::Bioengineering |
| url | http://hdl.handle.net/10356/52995 |
| work_keys_str_mv | AT lacamchung segmentationoflivertumorfromctimages |