Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images
In diagnosing interstitial lung disease (ILD) using HRCT Thorax images, the radiologists required to view large volume of images (30 slices scanned at 10 mm interval or 300 slices scanned at 1 mm interval). However, in the development of scoring index to assess the severity of the disease, viewing 3...
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| Format: | Conference or Workshop Item |
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2013
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| _version_ | 1796859614210293760 |
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| author | Mohd. Noor, N. Mohd. Rijal, O. Ming, J. T. C. Roseli, F. A. Ebrahimian, H. Kassim, R. M. Yunus, A. |
| author_facet | Mohd. Noor, N. Mohd. Rijal, O. Ming, J. T. C. Roseli, F. A. Ebrahimian, H. Kassim, R. M. Yunus, A. |
| author_sort | Mohd. Noor, N. |
| collection | ePrints |
| description | In diagnosing interstitial lung disease (ILD) using HRCT Thorax images, the radiologists required to view large volume of images (30 slices scanned at 10 mm interval or 300 slices scanned at 1 mm interval). However, in the development of scoring index to assess the severity of the disease, viewing 3 to 5 slices at the predetermined levels of the lung is suffice for the radiologist. To develop an algorithm to determine the severity of the ILD, it is important for the computer aided system to capture the main anatomy of the chest, namely the lung and heart at these 5 predetermined levels. In this paper, an automatic segmentation algorithm is proposed to obtain the shape of the heart and lung. In determine the quality of the segmentation, ground truth or manual tracing of the lung and heart boundary done by senior radiologist was compared with the result from the proposed automatic segmentation. This paper discussed five segmentation quality measurements that are used to measure the performance of the proposed segmentation algorithm, namely, the volume overlap error rate (VOE), relative volumetric agreement (RVA), average symmetric surface distance (ASSD), root mean square surface distance (RMSD) and Hausdorff distance (HD). The results showed that the proposed segmentation algorithm produced good quality segmentation for both right and left lung and may be used in the development of computer aided system application. |
| first_indexed | 2024-03-05T19:29:46Z |
| format | Conference or Workshop Item |
| id | utm.eprints-51295 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:29:46Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | utm.eprints-512952017-09-18T00:54:02Z http://eprints.utm.my/51295/ Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images Mohd. Noor, N. Mohd. Rijal, O. Ming, J. T. C. Roseli, F. A. Ebrahimian, H. Kassim, R. M. Yunus, A. T Technology In diagnosing interstitial lung disease (ILD) using HRCT Thorax images, the radiologists required to view large volume of images (30 slices scanned at 10 mm interval or 300 slices scanned at 1 mm interval). However, in the development of scoring index to assess the severity of the disease, viewing 3 to 5 slices at the predetermined levels of the lung is suffice for the radiologist. To develop an algorithm to determine the severity of the ILD, it is important for the computer aided system to capture the main anatomy of the chest, namely the lung and heart at these 5 predetermined levels. In this paper, an automatic segmentation algorithm is proposed to obtain the shape of the heart and lung. In determine the quality of the segmentation, ground truth or manual tracing of the lung and heart boundary done by senior radiologist was compared with the result from the proposed automatic segmentation. This paper discussed five segmentation quality measurements that are used to measure the performance of the proposed segmentation algorithm, namely, the volume overlap error rate (VOE), relative volumetric agreement (RVA), average symmetric surface distance (ASSD), root mean square surface distance (RMSD) and Hausdorff distance (HD). The results showed that the proposed segmentation algorithm produced good quality segmentation for both right and left lung and may be used in the development of computer aided system application. 2013 Conference or Workshop Item PeerReviewed Mohd. Noor, N. and Mohd. Rijal, O. and Ming, J. T. C. and Roseli, F. A. and Ebrahimian, H. and Kassim, R. M. and Yunus, A. (2013) Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images. In: Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). https://doi.org/10.1007/978-3-319-02958-0_16 |
| spellingShingle | T Technology Mohd. Noor, N. Mohd. Rijal, O. Ming, J. T. C. Roseli, F. A. Ebrahimian, H. Kassim, R. M. Yunus, A. Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images |
| title | Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images |
| title_full | Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images |
| title_fullStr | Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images |
| title_full_unstemmed | Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images |
| title_short | Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images |
| title_sort | segmentation of the lung anatomy for high resolution computed tomography hrct thorax images |
| topic | T Technology |
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