Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms
Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme f...
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| Format: | Article |
| Language: | English |
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Wiley
2009-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2009/767805 |
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| _version_ | 1832561236335656960 |
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| author | Imad Zyout Ikhlas Abdel-Qader Christina Jacobs |
| author_facet | Imad Zyout Ikhlas Abdel-Qader Christina Jacobs |
| author_sort | Imad Zyout |
| collection | DOAJ |
| description | Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation. |
| format | Article |
| id | doaj-art-a06cf45024084f9ba366c5111254eba2 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-a06cf45024084f9ba366c5111254eba22025-02-03T01:25:35ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962009-01-01200910.1155/2009/767805767805Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital MammogramsImad Zyout0Ikhlas Abdel-Qader1Christina Jacobs2Department of Electrical and Computer Engineering, Western Michigan University, MI 49008, USADepartment of Electrical and Computer Engineering, Western Michigan University, MI 49008, USARadiology Department, Bronson Methodist Hospital, Kalamazoo, MI 49007, USADetection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation.http://dx.doi.org/10.1155/2009/767805 |
| spellingShingle | Imad Zyout Ikhlas Abdel-Qader Christina Jacobs Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms International Journal of Biomedical Imaging |
| title | Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms |
| title_full | Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms |
| title_fullStr | Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms |
| title_full_unstemmed | Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms |
| title_short | Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms |
| title_sort | bayesian classifier with simplified learning phase for detecting microcalcifications in digital mammograms |
| url | http://dx.doi.org/10.1155/2009/767805 |
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