A new Local Radon Descriptor for Content-Based Image Search
This work addresses the need for efficient and discriminative image descriptors in medical expert systems, though it appears incremental as it builds on existing histogram-based methods.
The authors tackled the problem of content-based image retrieval in medical imaging by introducing a new local Radon descriptor and a fast convolution-based estimator, achieving superior results compared to existing histogram-based descriptors and some pre-trained CNNs on pathology and lung CT datasets.
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.