A Bag of Visual Words Model for Medical Image Retrieval

TL;DR

This paper introduces a Bag of Visual Words model tailored for medical image retrieval, leveraging intrinsic image features and supervised learning to improve retrieval accuracy in complex, multi-dimensional medical imaging data.

Contribution

The paper presents a novel MedIR approach that applies BoVW features with supervised classification to enhance content-based medical image retrieval accuracy.

Findings

Achieved a Mean Average Precision of 88.89% in top-3 retrieval

Effectively captures intrinsic features of multi-dimensional medical images

Improves semantic relevance and label uniformity in retrieval results

Abstract

Medical Image Retrieval is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content. Traditional models often fail to take the intrinsic characteristics of data into consideration, and have thus achieved limited accuracy when applied to medical images. The Bag of Visual Words (BoVW) is a technique that can be used to effectively represent intrinsic image features in vector space, so that applications like image classification and similar-image search can be optimized. In this paper, we present a MedIR approach based on the BoVW model for content-based medical image retrieval. As medical images as multi-dimensional, they exhibit underlying cluster and manifold information which enhances semantic relevance and allows for label uniformity. Hence, the BoVW features extracted for each image are used to train a supervised machine learning classifier based on positive and negative training images, for extending content based image retrieval. During experimental validation, the proposed model performed very well, achieving a Mean Average Precision of 88.89% during top-3 image retrieval experiments.