Breast Ultrasound Automated ROI Segmentation with Region Growing
Image segmentation is an important technology used in different areas ranging from image processing to image analysis. One of the simplest methods for image segmentation that is widely implemented in medical images is the region growing method. Current researches mostly focus on using the region...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/10665/ http://umpir.ump.edu.my/id/eprint/10665/1/Breast%20ultrasound%20automated%20ROI%20segmentation%20with.pdf |
| _version_ | 1848818804707033088 |
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| author | Lee, Lay-Khoon Liew, Siau-Chuin |
| author_facet | Lee, Lay-Khoon Liew, Siau-Chuin |
| author_sort | Lee, Lay-Khoon |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Image segmentation is an important technology used in different areas ranging from image processing to image
analysis. One of the simplest methods for image segmentation
that is widely implemented in medical images is the region
growing method. Current researches mostly focus on using the
region growing method to automatically detect the presence of tumor in MRI (Magnetic Resonance) images instead of
ultrasound images. In this paper, we present an algorithm to
automatically detect tumors in ultrasound images. Inspired by SergeBeucher and Balasubramanian’s road segmentation
algorithm, this paper will implement the road segmentation
algorithm into medical image segmentation. Results show that, the road segmentation algorithm actually works on the
segmentation of medical image. The dice coefficient was used to evaluate the accuracy of the algorithm, eventually getting a value of 0.988 ± 0.00147 as the mean and standard deviation. This value is significant, because the higher the DC value, the more accurate is the segmentation. Besides that, the DC value can use for future reference and comparison. |
| first_indexed | 2025-11-15T01:43:29Z |
| format | Conference or Workshop Item |
| id | ump-10665 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:43:29Z |
| publishDate | 2015 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-106652016-07-21T04:33:21Z http://umpir.ump.edu.my/id/eprint/10665/ Breast Ultrasound Automated ROI Segmentation with Region Growing Lee, Lay-Khoon Liew, Siau-Chuin Q Science (General) QA76 Computer software Image segmentation is an important technology used in different areas ranging from image processing to image analysis. One of the simplest methods for image segmentation that is widely implemented in medical images is the region growing method. Current researches mostly focus on using the region growing method to automatically detect the presence of tumor in MRI (Magnetic Resonance) images instead of ultrasound images. In this paper, we present an algorithm to automatically detect tumors in ultrasound images. Inspired by SergeBeucher and Balasubramanian’s road segmentation algorithm, this paper will implement the road segmentation algorithm into medical image segmentation. Results show that, the road segmentation algorithm actually works on the segmentation of medical image. The dice coefficient was used to evaluate the accuracy of the algorithm, eventually getting a value of 0.988 ± 0.00147 as the mean and standard deviation. This value is significant, because the higher the DC value, the more accurate is the segmentation. Besides that, the DC value can use for future reference and comparison. IEEE 2015-08 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/10665/1/Breast%20ultrasound%20automated%20ROI%20segmentation%20with.pdf Lee, Lay-Khoon and Liew, Siau-Chuin (2015) Breast Ultrasound Automated ROI Segmentation with Region Growing. In: IEEE 4th International Conference on Software Engineering and Computer Systems , 19-21 August 2015 , Kuantan, Pahang, Malaysia. pp. 177-182.. ISBN 978-1-4673-6722-6 (Published) http://dx.doi.org/10.1109/ICSECS.2015.7333106 |
| spellingShingle | Q Science (General) QA76 Computer software Lee, Lay-Khoon Liew, Siau-Chuin Breast Ultrasound Automated ROI Segmentation with Region Growing |
| title | Breast Ultrasound Automated ROI Segmentation with Region Growing |
| title_full | Breast Ultrasound Automated ROI Segmentation with Region Growing |
| title_fullStr | Breast Ultrasound Automated ROI Segmentation with Region Growing |
| title_full_unstemmed | Breast Ultrasound Automated ROI Segmentation with Region Growing |
| title_short | Breast Ultrasound Automated ROI Segmentation with Region Growing |
| title_sort | breast ultrasound automated roi segmentation with region growing |
| topic | Q Science (General) QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/10665/ http://umpir.ump.edu.my/id/eprint/10665/ http://umpir.ump.edu.my/id/eprint/10665/1/Breast%20ultrasound%20automated%20ROI%20segmentation%20with.pdf |