A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM
In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature...
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/970287 |
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| author | Xin-Sheng Zhang |
| author_facet | Xin-Sheng Zhang |
| author_sort | Xin-Sheng Zhang |
| collection | DOAJ |
| description | In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the lP-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods. |
| format | Article |
| id | doaj-art-4acff071b46249be95e2695a05d5518d |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-4acff071b46249be95e2695a05d5518d2025-08-20T02:38:03ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/970287970287A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVMXin-Sheng Zhang0School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, ChinaIn digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the lP-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods.http://dx.doi.org/10.1155/2014/970287 |
| spellingShingle | Xin-Sheng Zhang A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM The Scientific World Journal |
| title | A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_full | A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_fullStr | A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_full_unstemmed | A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_short | A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM |
| title_sort | new approach for clustered mcs classification with sparse features learning and twsvm |
| url | http://dx.doi.org/10.1155/2014/970287 |
| work_keys_str_mv | AT xinshengzhang anewapproachforclusteredmcsclassificationwithsparsefeatureslearningandtwsvm AT xinshengzhang newapproachforclusteredmcsclassificationwithsparsefeatureslearningandtwsvm |