Evolutionary Projection Selection for Radon Barcodes

TL;DR

This paper introduces an evolutionary method to select optimal Radon projections for generating more expressive barcodes in medical image tagging, improving over traditional equidistant projection selection.

Contribution

It proposes an evolutionary approach to select the most informative projections for Radon barcodes, enhancing their discriminative power for medical image annotation.

Findings

Evolutionary projection selection outperforms equidistant projections.

Selected projections increase barcode expressiveness.

Method tested on IRMA dataset with promising results.

Abstract

Recently, Radon transformation has been used to generate barcodes for tagging medical images. The under-sampled image is projected in certain directions, and each projection is binarized using a local threshold. The concatenation of the thresholded projections creates a barcode that can be used for tagging or annotating medical images. A small number of equidistant projections, e.g., 4 or 8, is generally used to generate short barcodes. However, due to the diverse nature of digital images, and since we are only working with a small number of projections (to keep the barcode short), taking equidistant projections may not be the best course of action. In this paper, we proposed to find $n$ optimal projections, whereas $n\!<\!180$, in order to increase the expressiveness of Radon barcodes. We show examples for the exhaustive search for the simple case when we attempt to find 4 best projections out of 16 equidistant projections and compare it with the evolutionary approach in order to establish the benefit of the latter when operating on a small population size as in the case of micro-DE. We randomly selected 10 different classes from IRMA dataset (14,400 x-ray images in 58 classes) and further randomly selected 5 images per class for our tests.