Local Radon Descriptors for Image Search

TL;DR

This paper introduces Local Radon Descriptors (LRD), a novel image descriptor that improves medical image retrieval by focusing on local Radon projections, outperforming global Radon descriptors and matching established methods.

Contribution

The paper proposes the LRD method, demonstrating its superior discrimination capability and retrieval performance over global Radon descriptors in medical image datasets.

Findings

LRD significantly outperforms global Radon descriptors in image retrieval.

LRD achieves comparable results to LBP and HOG descriptors.

Applying LRD to large medical datasets improves retrieval accuracy.

Abstract

Radon transform and its inverse operation are important techniques in medical imaging tasks. Recently, there has been renewed interest in Radon transform for applications such as content-based medical image retrieval. However, all studies so far have used Radon transform as a global or quasi-global image descriptor by extracting projections of the whole image or large sub-images. This paper attempts to show that the dense sampling to generate the histogram of local Radon projections has a much higher discrimination capability than the global one. In this paper, we introduce Local Radon Descriptor (LRD) and apply it to the IRMA dataset, which contains 14,410 x-ray images as well as to the INRIA Holidays dataset with 1,990 images. Our results show significant improvement in retrieval performance by using LRD versus its global version. We also demonstrate that LRD can deliver results comparable to well-established descriptors like LBP and HOG.