@inproceedings{a5401fdde3b145c48feedce434a8a856,
title = "Combining real-valued and binary Gabor-Radon features for classification and search in medical imaging archives",
abstract = "Content-based image retrieval (CBIR) of medical images in large datasets to identify similar images when a query image is given can be very useful in improving the diagnostic decision of the clinical experts and as well in educational scenarios. In this paper, we used two stage classification and retrieval approach to retrieve similar images. First, the Gabor filters are applied to Radon-transformed images to extract features and to train a multi-class SVM. Then based on the classification results and using an extracted Gabor barcode, similar images are retrieved. The proposed method was tested on IRMA dataset which contains more than 14,000 images. Experimental results show the efficiency of our approach in retrieving similar images compared to other Gabor-Radon-oriented methods.",
keywords = "Gabor filter, Image retrieval, Radon Transform, barcodes, medical imaging",
author = "Hamed Erfankhah and Mehran Yazdi and Tizhoosh, {H. R.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 ; Conference date: 27-11-2017 Through 01-12-2017",
year = "2018",
month = feb,
day = "2",
doi = "10.1109/SSCI.2017.8285389",
language = "English (US)",
series = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",
}