@inproceedings{29f1ad9c2f654b3aba84bd9607a93112,
title = "A New Local Radon Descriptor for Content-Based Image Search",
abstract = "Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this paper, we introduce a new simple descriptor based on the histogram of local Radon projections. We also propose a very fast convolution-based local Radon estimator to overcome the slow process of Radon projections. We performed our experiments using pathology images (KimiaPath24) and lung CT patches and test our proposed solution for medical image processing. We achieved superior results compared with other histogram-based descriptors such as LBP and HoG as well as some pre-trained CNNs.",
keywords = "Image retrieval, Local radon, Medical imaging",
author = "Morteza Babaie and Hany Kashani and Kumar, {Meghana D.} and Tizhoosh, {H. R.}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 18th International Conference on Artificial Intelligence in Medicine, AIME 2020 ; Conference date: 25-08-2020 Through 28-08-2020",
year = "2020",
doi = "10.1007/978-3-030-59137-3_41",
language = "English (US)",
isbn = "9783030591366",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "463--472",
editor = "Martin Michalowski and Robert Moskovitch",
booktitle = "Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings",
}