A visual analytics approach using the exploration of multidimensional feature spaces for content-based medical image retrieval

Ashnil Kumar, Falk Nette, Karsten Klein, Michael Fulham, Jinman Kim

    Research output: Contribution to journalArticleResearchpeer-review

    21 Citations (Scopus)

    Abstract

    Content-based image retrieval (CBIR) is a search technique based on the similarity of visual features and has demonstrated potential benefits for medical diagnosis, education, and research. However, clinical adoption of CBIR is partially hindered by the difference between the computed image similarity and the user s search intent, the semantic gap, with the end result that relevant images with outlier features may not be retrieved. Furthermore, most CBIR algorithms do not provide intuitive explanations as to why the retrieved images were considered similar to the query (e.g., which subset of features were similar), hence, it is difficult for users to verify if relevant images, with a small subset of outlier features, were missed. Users, therefore, resort to examining irrelevant images and there are limited opportunities to discover these ?missed? images. In this paper, we propose a new approach to medical CBIR by enabling a guided visual exploration of the search space through a tool, called visual analytics for medical image retrieval (VAMIR). The visual analytics approach facilitates interactive exploration of the entire dataset using the query image as a point-of-reference. We conducted a user study and several case studies to demonstrate the capabilities of VAMIR in the retrieval of computed tomography images and multimodality positron emission tomography and computed tomography images.
    Original languageEnglish
    Pages (from-to)1734 - 1746
    Number of pages13
    JournalIEEE Journal of Biomedical and Health Informatics
    Volume19
    Issue number5
    DOIs
    Publication statusPublished - 2015

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