Abstract
With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF algorithms interactively learn a user’s preferences to bridge the “semantic gap” between low-level computerized visual features and high-level human semantic perception and thus improve retrieval performance. However, most existing RF algorithms perform in the original high-dimensional feature space and ignore the manifold structure of the low-level visual features of images. In this paper, we propose a new method, termed dual-force ISOMAP (DFISOMAP), for content-based medical image retrieval. Under the assumption that medical images lie on a low-dimensional manifold embedded in a high-dimensional ambient space, DFISOMAP operates in the following three stages. First, the geometric structure of positive examples in the learned low-dimensional embedding is preserved according to the isometric feature mapping (ISOMAP) criterion. To precisely model the geometric structure, a reconstruction error constraint is also added. Second, the average distance between positive and negative examples is maximized to separate them; this margin maximization acts as a force that pushes negative examples far away from positive examples. Finally, the similarity propagation technique is utilized to provide negative examples with another force that will pull them back into the negative sample set. We evaluate the proposed method on a subset of the IRMA medical image dataset with a RF-based medical image retrieval framework. Experimental results show that DFISOMAP outperforms popular approaches for content-based medical image retrieval in terms of accuracy and stability.
Highlights
Medical image interpretation is a process which incorporates subjective perception and objective reasoning
The goal of content-based medical image retrieval (CBMIR) is to enable radiologists to make better diagnosis about a given case by retrieving similar cases from a variety of semantically annotated medical image archives. It is well-known that ‘‘semantic gap’’ is one of the issues faced by content-based image retrieval (CBIR)
The fact that medical images contain varied, rich and subtle visual features [3] is an additional challenge to the use of CBIR in radiology
Summary
Medical image interpretation is a process which incorporates subjective perception and objective reasoning. Though the approaches mentioned above achieve promising results, there is room for performance enhancement because most of these methods do not consider the manifold structure of low-level image features. Description medical image dataset high-dimensional ambient space low-dimensional embedding relevance feedback set in Rh positive relevance feedback set negative relevance feedback set relevance feedback set in Rl projection matrix, Y ~U T X identity matrix reconstruction coefficient matrix in LLE linear product matrix of D doi:10.1371/journal.pone.0084096.t001
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