Gabor Barcodes for Medical Image Retrieval

TL;DR

This paper introduces Gabor Barcodes, a new binary descriptor for medical image retrieval that leverages Gabor transform features for robust, efficient annotation and retrieval in large medical image databases.

Contribution

The paper proposes Gabor Barcodes, a novel texture-based binary descriptor that improves robustness and discriminative power for medical image annotation and retrieval.

Findings

Achieved a total error score of 351 on IRMA dataset.

Demonstrated robustness against rotation, scale, and illumination changes.

Improved retrieval accuracy with Gabor Barcodes compared to existing methods.

Abstract

In recent years, advances in medical imaging have led to the emergence of massive databases, containing images from a diverse range of modalities. This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side. Binary descriptors have recently gained more attention as a potential vehicle to achieve these goals. One of the recently introduced binary descriptors for tagging of medical images are Radon barcodes (RBCs) that are driven from Radon transform via local thresholding. Gabor transform is also a powerful transform to extract texture-based information. Gabor features have exhibited robustness against rotation, scale, and also photometric disturbances, such as illumination changes and image noise in many applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework for the image annotation. To find the most discriminative GBC for a given query image, the effects of employing Gabor filters with different parameters, i.e., different sets of scales and orientations, are investigated, resulting in different barcode lengths and retrieval performances. The proposed method has been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray images for indexing, and 1,733 x-rays images for testing. A total error score as low as $351$ ($\approx 80\%$ accuracy for the first hit) was achieved.