Radon Features and Barcodes for Medical Image Retrieval via SVM

TL;DR

This paper introduces a medical image retrieval method combining Radon projections, barcodes, and SVM classification, achieving fast, accurate retrieval with low memory use on large x-ray datasets.

Contribution

It presents a novel combination of Radon features, barcodes, and SVM for efficient content-based medical image retrieval.

Findings

High retrieval accuracy demonstrated on IRMA dataset

Method is fast with low memory requirements

Effective even with SVM classification errors

Abstract

For more than two decades, research has been performed on content-based image retrieval (CBIR). By combining Radon projections and the support vector machines (SVM), a content-based medical image retrieval method is presented in this work. The proposed approach employs the normalized Radon projections with corresponding image category labels to build an SVM classifier, and the Radon barcode database which encodes every image in a binary format is also generated simultaneously to tag all images. To retrieve similar images when a query image is given, Radon projections and the barcode of the query image are generated. Subsequently, the k-nearest neighbor search method is applied to find the images with minimum Hamming distance of the Radon barcode within the same class predicted by the trained SVM classifier that uses Radon features. The performance of the proposed method is validated by using the IRMA 2009 dataset with 14,410 x-ray images in 57 categories. The results demonstrate that our method has the capacity to retrieve similar responses for the correctly identified query image and even for those mistakenly classified by SVM. The approach further is very fast and has low memory requirement.