Microcalcifications segmentation using three edge detection techniques
Edge detection has been widely used especially in medical image processing field. In this paper we are comparing Sobel, Prewitt and Laplacian of Gaussian (LoG) edge detection techniques in segmenting the boundary of microcalcifications. The edge detection must satisfy the breast phantom scoring crit...
| Main Authors: | , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2012
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| Online Access: | http://psasir.upm.edu.my/id/eprint/39272/1/Microcalcifications%20segmentation%20using%20three%20edge%20detection%20techniques.pdf |
| _version_ | 1825949379830218752 |
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| author | Yasiran, Siti Salmah Jumaat, Abdul Kadir Abdul Malek, Aminah Hashim, Fatin Hanani Nasrir, Nor Dhaniah Sayed Hassan, Syarifah Nurul Azirah Ahmad, Normah Mahmud, Rozi |
| author_facet | Yasiran, Siti Salmah Jumaat, Abdul Kadir Abdul Malek, Aminah Hashim, Fatin Hanani Nasrir, Nor Dhaniah Sayed Hassan, Syarifah Nurul Azirah Ahmad, Normah Mahmud, Rozi |
| author_sort | Yasiran, Siti Salmah |
| collection | UPM |
| description | Edge detection has been widely used especially in medical image processing field. In this paper we are comparing Sobel, Prewitt and Laplacian of Gaussian (LoG) edge detection techniques in segmenting the boundary of microcalcifications. The edge detection must satisfy the breast phantom scoring criteria before the segmentation phase is carried out. Then, all of the edge detection techniques are implemented in the Enhanced Distance Active Contour (EDAC) model for the segmentation process. Results obtained from Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve shows that the Prewitt edge detection has the highest value of AUC, followed by the Sobel and LoG which are 0.79, 0.72 and 0.71 respectively. |
| first_indexed | 2024-03-06T08:43:40Z |
| format | Conference or Workshop Item |
| id | upm.eprints-39272 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:43:40Z |
| publishDate | 2012 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-392722016-10-31T07:10:36Z http://psasir.upm.edu.my/id/eprint/39272/ Microcalcifications segmentation using three edge detection techniques Yasiran, Siti Salmah Jumaat, Abdul Kadir Abdul Malek, Aminah Hashim, Fatin Hanani Nasrir, Nor Dhaniah Sayed Hassan, Syarifah Nurul Azirah Ahmad, Normah Mahmud, Rozi Edge detection has been widely used especially in medical image processing field. In this paper we are comparing Sobel, Prewitt and Laplacian of Gaussian (LoG) edge detection techniques in segmenting the boundary of microcalcifications. The edge detection must satisfy the breast phantom scoring criteria before the segmentation phase is carried out. Then, all of the edge detection techniques are implemented in the Enhanced Distance Active Contour (EDAC) model for the segmentation process. Results obtained from Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve shows that the Prewitt edge detection has the highest value of AUC, followed by the Sobel and LoG which are 0.79, 0.72 and 0.71 respectively. IEEE 2012 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/39272/1/Microcalcifications%20segmentation%20using%20three%20edge%20detection%20techniques.pdf Yasiran, Siti Salmah and Jumaat, Abdul Kadir and Abdul Malek, Aminah and Hashim, Fatin Hanani and Nasrir, Nor Dhaniah and Sayed Hassan, Syarifah Nurul Azirah and Ahmad, Normah and Mahmud, Rozi (2012) Microcalcifications segmentation using three edge detection techniques. In: IEEE International Conference on Electronics Design, Systems and Applications (ICEDSA 2012), 5-6 Nov. 2012, Seri Pacific Hotel, Kuala Lumpur. (pp. 207-211). 10.1109/ICEDSA.2012.6507798 |
| spellingShingle | Yasiran, Siti Salmah Jumaat, Abdul Kadir Abdul Malek, Aminah Hashim, Fatin Hanani Nasrir, Nor Dhaniah Sayed Hassan, Syarifah Nurul Azirah Ahmad, Normah Mahmud, Rozi Microcalcifications segmentation using three edge detection techniques |
| title | Microcalcifications segmentation using three edge detection techniques |
| title_full | Microcalcifications segmentation using three edge detection techniques |
| title_fullStr | Microcalcifications segmentation using three edge detection techniques |
| title_full_unstemmed | Microcalcifications segmentation using three edge detection techniques |
| title_short | Microcalcifications segmentation using three edge detection techniques |
| title_sort | microcalcifications segmentation using three edge detection techniques |
| url | http://psasir.upm.edu.my/id/eprint/39272/1/Microcalcifications%20segmentation%20using%20three%20edge%20detection%20techniques.pdf |
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