Khan, S. and Yong, S.-P. and Deng, J.D. (2016) Ensemble classification with modified SIFT descriptor for medical image modality. In: UNSPECIFIED.
Full text not available from this repository.Abstract
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.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 0; Conference of 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015 ; Conference Date: 23 November 2015 Through 24 November 2015; Conference Code:125066 |
Uncontrolled Keywords: | Image enhancement; Medical computing; Medical imaging, Bag-of-visual-words; Conventional approach; Ensemble classification; Ensemble classifiers; Image Descriptor; Imaging modality; Local binary patterns; Medical database, Image classification |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 09 Nov 2023 16:18 |
Last Modified: | 09 Nov 2023 16:18 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/6580 |