Coronary artery segmentation in angiograms with pattern recognition techniques - a survey
Medical image processing is nowadays one of the best tools to make an informative model from a raw image of each part of the body, and segmentation is the most important step in which used to extract significant features. Coronary artery segmentation algorithm in angiograms is a fundamental componen...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41299/1/Coronary%20artery%20segmentation%20in%20angiograms%20with%20pattern%20recognition%20techniques%20-%20a%20survey.pdf |
| _version_ | 1825949759299387392 |
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| author | Tayebi, Rohollah Moosavi Sulaiman, Puteri Suhaiza O. K. Rahmat, Rahmita Wirza Dimon, Mohd Zamrin Kadiman, Suhaini Abdullah, Lilly Nurliyana Mazaheri, Samaneh |
| author_facet | Tayebi, Rohollah Moosavi Sulaiman, Puteri Suhaiza O. K. Rahmat, Rahmita Wirza Dimon, Mohd Zamrin Kadiman, Suhaini Abdullah, Lilly Nurliyana Mazaheri, Samaneh |
| author_sort | Tayebi, Rohollah Moosavi |
| collection | UPM |
| description | Medical image processing is nowadays one of the best tools to make an informative model from a raw image of each part of the body, and segmentation is the most important step in which used to extract significant features. Coronary artery segmentation algorithm in angiograms is a fundamental component of each cardiac image processing system. There are lots of techniques and algorithms proposed for extracting coronary arteries in angiograms. But based on our knowledge, there is not any review paper to categorize and compare them together. In this paper, we have divided these algorithms into five major classes and propose a survey for the main class, pattern recognition, which is a famous technique in this manner. We studied all the papers in the pattern recognition class and defined six categories for them: (1) Multi scale approaches (2) Region growing approaches (3) Matching filters approaches (4) Mathematical morphology approaches (5) Skeleton based approaches and (6) Ridge based approaches. Finally, we made a table to compare all the algorithms in each category against criteria such as: user interaction, angiography types, dimensionality, enhancement method, full coronary artery output, whole tree output, and 3D reconstruction ability. |
| first_indexed | 2024-03-06T08:49:30Z |
| format | Conference or Workshop Item |
| id | upm.eprints-41299 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:49:30Z |
| publishDate | 2013 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-412992018-10-24T01:23:07Z http://psasir.upm.edu.my/id/eprint/41299/ Coronary artery segmentation in angiograms with pattern recognition techniques - a survey Tayebi, Rohollah Moosavi Sulaiman, Puteri Suhaiza O. K. Rahmat, Rahmita Wirza Dimon, Mohd Zamrin Kadiman, Suhaini Abdullah, Lilly Nurliyana Mazaheri, Samaneh Medical image processing is nowadays one of the best tools to make an informative model from a raw image of each part of the body, and segmentation is the most important step in which used to extract significant features. Coronary artery segmentation algorithm in angiograms is a fundamental component of each cardiac image processing system. There are lots of techniques and algorithms proposed for extracting coronary arteries in angiograms. But based on our knowledge, there is not any review paper to categorize and compare them together. In this paper, we have divided these algorithms into five major classes and propose a survey for the main class, pattern recognition, which is a famous technique in this manner. We studied all the papers in the pattern recognition class and defined six categories for them: (1) Multi scale approaches (2) Region growing approaches (3) Matching filters approaches (4) Mathematical morphology approaches (5) Skeleton based approaches and (6) Ridge based approaches. Finally, we made a table to compare all the algorithms in each category against criteria such as: user interaction, angiography types, dimensionality, enhancement method, full coronary artery output, whole tree output, and 3D reconstruction ability. IEEE 2013 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/41299/1/Coronary%20artery%20segmentation%20in%20angiograms%20with%20pattern%20recognition%20techniques%20-%20a%20survey.pdf Tayebi, Rohollah Moosavi and Sulaiman, Puteri Suhaiza and O. K. Rahmat, Rahmita Wirza and Dimon, Mohd Zamrin and Kadiman, Suhaini and Abdullah, Lilly Nurliyana and Mazaheri, Samaneh (2013) Coronary artery segmentation in angiograms with pattern recognition techniques - a survey. In: 2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2013), 23-24 Dec. 2013, Kuching, Sarawak, Malaysia. (pp. 321-326). 10.1109/ACSAT.2013.70 |
| spellingShingle | Tayebi, Rohollah Moosavi Sulaiman, Puteri Suhaiza O. K. Rahmat, Rahmita Wirza Dimon, Mohd Zamrin Kadiman, Suhaini Abdullah, Lilly Nurliyana Mazaheri, Samaneh Coronary artery segmentation in angiograms with pattern recognition techniques - a survey |
| title | Coronary artery segmentation in angiograms with pattern recognition techniques - a survey |
| title_full | Coronary artery segmentation in angiograms with pattern recognition techniques - a survey |
| title_fullStr | Coronary artery segmentation in angiograms with pattern recognition techniques - a survey |
| title_full_unstemmed | Coronary artery segmentation in angiograms with pattern recognition techniques - a survey |
| title_short | Coronary artery segmentation in angiograms with pattern recognition techniques - a survey |
| title_sort | coronary artery segmentation in angiograms with pattern recognition techniques a survey |
| url | http://psasir.upm.edu.my/id/eprint/41299/1/Coronary%20artery%20segmentation%20in%20angiograms%20with%20pattern%20recognition%20techniques%20-%20a%20survey.pdf |
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