An approach for automatic lesion detection in mammograms
Early stage breast cancer detection can reduce death rates in long term. Mammography is the current standard screening tool available for breast cancer detection, but it is found to have high false-positive and false-negative rates. This may be due to poor quality of mammograms, subtle nature of mal...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2018.1444320 |
| _version_ | 1797721640338980864 |
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| author | K.U. Sheba S. Gladston Raj |
| author_facet | K.U. Sheba S. Gladston Raj |
| author_sort | K.U. Sheba |
| collection | DOAJ |
| description | Early stage breast cancer detection can reduce death rates in long term. Mammography is the current standard screening tool available for breast cancer detection, but it is found to have high false-positive and false-negative rates. This may be due to poor quality of mammograms, subtle nature of malignancies and limitations in human/brain visual system. The aim of this research work is to develop an efficient classification tool with improved breast screening accuracy to distinguish between healthy, benign and malignant breast parenchyma in digital mammograms. This paper presents a computer aided diagnosis system for automated detection and diagnosis of breast cancer in digital mammograms. The proposed system can be used as a reference reader for double reading the mammograms and thus assisting the radiologists in clinical diagnosis by indicating suspicious abnormalities. This can improve the diagnostic performance of the radiologists. In the proposed methodology, the regions of interest (ROI) are automatically detected and segmented from mammograms using global thresholding, Otsu’s method and morphological operations. Shape, texture and grey-level features are extracted from the ROIs. Optimal features are selected using Classifier and Regression Tree (CART). Classification is performed with Feed forward artificial neural networks using back propagation. Performance is evaluated using Receiver Operating Characteristic (ROC) analysis and confusion matrix. Experimental results show that the proposed method achieved an accuracy of 96% with 83% sensitivity and 98% specificity. The proposed methodology has been compared with several other classification models and is found to have a good performance in terms of accuracy, sensitivity and specificity. |
| first_indexed | 2024-03-12T09:37:07Z |
| format | Article |
| id | doaj.art-683906b4574e4a7cb82fe28c87735aa0 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T09:37:07Z |
| publishDate | 2018-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-683906b4574e4a7cb82fe28c87735aa02023-09-02T13:33:54ZengTaylor & Francis GroupCogent Engineering2331-19162018-01-015110.1080/23311916.2018.14443201444320An approach for automatic lesion detection in mammogramsK.U. Sheba0S. Gladston Raj1BPC CollegeGovernment CollegeEarly stage breast cancer detection can reduce death rates in long term. Mammography is the current standard screening tool available for breast cancer detection, but it is found to have high false-positive and false-negative rates. This may be due to poor quality of mammograms, subtle nature of malignancies and limitations in human/brain visual system. The aim of this research work is to develop an efficient classification tool with improved breast screening accuracy to distinguish between healthy, benign and malignant breast parenchyma in digital mammograms. This paper presents a computer aided diagnosis system for automated detection and diagnosis of breast cancer in digital mammograms. The proposed system can be used as a reference reader for double reading the mammograms and thus assisting the radiologists in clinical diagnosis by indicating suspicious abnormalities. This can improve the diagnostic performance of the radiologists. In the proposed methodology, the regions of interest (ROI) are automatically detected and segmented from mammograms using global thresholding, Otsu’s method and morphological operations. Shape, texture and grey-level features are extracted from the ROIs. Optimal features are selected using Classifier and Regression Tree (CART). Classification is performed with Feed forward artificial neural networks using back propagation. Performance is evaluated using Receiver Operating Characteristic (ROC) analysis and confusion matrix. Experimental results show that the proposed method achieved an accuracy of 96% with 83% sensitivity and 98% specificity. The proposed methodology has been compared with several other classification models and is found to have a good performance in terms of accuracy, sensitivity and specificity.http://dx.doi.org/10.1080/23311916.2018.1444320grey-level co-occurrence matrixgrey level run length matrixotsu’s methodmorphological openfeed forward artificial neural networksglobal thresholding |
| spellingShingle | K.U. Sheba S. Gladston Raj An approach for automatic lesion detection in mammograms Cogent Engineering grey-level co-occurrence matrix grey level run length matrix otsu’s method morphological open feed forward artificial neural networks global thresholding |
| title | An approach for automatic lesion detection in mammograms |
| title_full | An approach for automatic lesion detection in mammograms |
| title_fullStr | An approach for automatic lesion detection in mammograms |
| title_full_unstemmed | An approach for automatic lesion detection in mammograms |
| title_short | An approach for automatic lesion detection in mammograms |
| title_sort | approach for automatic lesion detection in mammograms |
| topic | grey-level co-occurrence matrix grey level run length matrix otsu’s method morphological open feed forward artificial neural networks global thresholding |
| url | http://dx.doi.org/10.1080/23311916.2018.1444320 |
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