A new Local Radon Descriptor for Content-Based Image Search

TL;DR

This paper introduces a simple, fast local Radon descriptor for content-based image retrieval, especially in medical imaging, outperforming traditional histogram descriptors and some CNNs.

Contribution

A novel local Radon descriptor and a rapid convolution-based estimator that enhance medical image retrieval efficiency and accuracy.

Findings

Superior retrieval performance on pathology and lung CT images.

Outperforms LBP, HoG, and some pre-trained CNNs.

Faster Radon projection computation.

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.