Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives

TL;DR

This paper presents a two-stage CBIR method for medical images that combines Gabor-Radon features and barcodes, improving retrieval accuracy in large datasets like IRMA.

Contribution

It introduces a novel combination of real-valued Gabor-Radon features with binary barcodes for enhanced image classification and retrieval in medical imaging archives.

Findings

Outperforms other Gabor-Radon methods in retrieval accuracy

Effective on large datasets with over 14,000 images

Combines feature extraction and binary encoding for efficiency

Abstract

Content-based image retrieval (CBIR) of medical images in large datasets to identify similar images when a query image is given can be very useful in improving the diagnostic decision of the clinical experts and as well in educational scenarios. In this paper, we used two stage classification and retrieval approach to retrieve similar images. First, the Gabor filters are applied to Radon-transformed images to extract features and to train a multi-class SVM. Then based on the classification results and using an extracted Gabor barcode, similar images are retrieved. The proposed method was tested on IRMA dataset which contains more than 14,000 images. Experimental results show the efficiency of our approach in retrieving similar images compared to other Gabor-Radon-oriented methods.