Ensemble classification with modified SIFT descriptor for medical image modality
The increasing number of medical images of various imaging modalities is challenging the accuracy and efficiency of radiologists. In order to retrieve the images from medical databases, radiologists will confine their search to the image modality. In this paper, we present an improved image feature...
| Main Authors: | Khan, S., Yong, S.-P., Deng, J.D. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.30583 / |
| Published: |
IEEE Computer Society
2016
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006886571&doi=10.1109%2fIVCNZ.2015.7761517&partnerID=40&md5=0b269b0ecd8b5298587a758a53dc40ef http://eprints.utp.edu.my/30583/ |
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utp-eprints.305832022-03-25T07:11:55Z Ensemble classification with modified SIFT descriptor for medical image modality Khan, S. Yong, S.-P. Deng, J.D. The increasing number of medical images of various imaging modalities is challenging the accuracy and efficiency of radiologists. In order to retrieve the images from medical databases, radiologists will confine their search to the image modality. In this paper, we present an improved image feature to represent medical images for image modality classification. The proposed image descriptor is an ensemble descriptor that combines the Harris Corner encoded by the SIFT algorithm fused with Local Binary Pattern. Furthermore, we propose an ensemble classifier with surrogate splits to be used in medical image modality classification in order to improve the performance. It is shown that the proposed ensemble classifier with surrogate splits and ensemble descriptor encoded with bag-of-visual-words representation outperforms other conventional approaches applied in medical image modality classification. © 2015 IEEE. IEEE Computer Society 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006886571&doi=10.1109%2fIVCNZ.2015.7761517&partnerID=40&md5=0b269b0ecd8b5298587a758a53dc40ef Khan, S. and Yong, S.-P. and Deng, J.D. (2016) Ensemble classification with modified SIFT descriptor for medical image modality. In: UNSPECIFIED. http://eprints.utp.edu.my/30583/ |
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Universiti Teknologi Petronas |
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UTP Institutional Repository |
| description |
The increasing number of medical images of various imaging modalities is challenging the accuracy and efficiency of radiologists. In order to retrieve the images from medical databases, radiologists will confine their search to the image modality. In this paper, we present an improved image feature to represent medical images for image modality classification. The proposed image descriptor is an ensemble descriptor that combines the Harris Corner encoded by the SIFT algorithm fused with Local Binary Pattern. Furthermore, we propose an ensemble classifier with surrogate splits to be used in medical image modality classification in order to improve the performance. It is shown that the proposed ensemble classifier with surrogate splits and ensemble descriptor encoded with bag-of-visual-words representation outperforms other conventional approaches applied in medical image modality classification. © 2015 IEEE. |
| format |
Conference or Workshop Item |
| author |
Khan, S. Yong, S.-P. Deng, J.D. |
| spellingShingle |
Khan, S. Yong, S.-P. Deng, J.D. Ensemble classification with modified SIFT descriptor for medical image modality |
| author_sort |
Khan, S. |
| title |
Ensemble classification with modified SIFT descriptor for medical image modality |
| title_short |
Ensemble classification with modified SIFT descriptor for medical image modality |
| title_full |
Ensemble classification with modified SIFT descriptor for medical image modality |
| title_fullStr |
Ensemble classification with modified SIFT descriptor for medical image modality |
| title_full_unstemmed |
Ensemble classification with modified SIFT descriptor for medical image modality |
| title_sort |
ensemble classification with modified sift descriptor for medical image modality |
| publisher |
IEEE Computer Society |
| publishDate |
2016 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006886571&doi=10.1109%2fIVCNZ.2015.7761517&partnerID=40&md5=0b269b0ecd8b5298587a758a53dc40ef http://eprints.utp.edu.my/30583/ |
| _version_ |
1741197431431757824 |
| score |
11.62408 |