Overview on subjective similarity of images for content-based medical image retrieval

Overview on subjective similarity of images for content-based medical image retrieval Computer-aided diagnosis systems for assisting the classification of various diseases have the potential to improve radiologists’ diagnostic accuracy and efficiency, as reported in several studies. Conventional systems generally provide the probabilities of disease types in terms of numerical values, a method that may not be efficient for radiologists who are trained by reading a large number of images. Presentation of reference images similar to those of a new case being diagnosed can supplement the probability outputs based on computerized analysis as an intuitive guide, and it can assist radiologists in their diagnosis, reporting, and treatment planning. Many studies on content-based medical image retrievals have been reported on. For retrieval of perceptually similar and diagnostically relevant images, incorporation of perceptual similarity data by radiologists has been suggested. In this paper, studies on image retrieval methods are reviewed with a special focus on quantification, utilization, and the evaluation of subjective similarities between pairs of images. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Radiological Physics and Technology Springer Journals

Overview on subjective similarity of images for content-based medical image retrieval

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Publisher
Springer Singapore
Copyright
Copyright © 2018 by Japanese Society of Radiological Technology and Japan Society of Medical Physics
Subject
Medicine & Public Health; Imaging / Radiology; Nuclear Medicine; Radiotherapy; Medical and Radiation Physics
ISSN
1865-0333
eISSN
1865-0341
D.O.I.
10.1007/s12194-018-0461-6
Publisher site
See Article on Publisher Site

Abstract

Computer-aided diagnosis systems for assisting the classification of various diseases have the potential to improve radiologists’ diagnostic accuracy and efficiency, as reported in several studies. Conventional systems generally provide the probabilities of disease types in terms of numerical values, a method that may not be efficient for radiologists who are trained by reading a large number of images. Presentation of reference images similar to those of a new case being diagnosed can supplement the probability outputs based on computerized analysis as an intuitive guide, and it can assist radiologists in their diagnosis, reporting, and treatment planning. Many studies on content-based medical image retrievals have been reported on. For retrieval of perceptually similar and diagnostically relevant images, incorporation of perceptual similarity data by radiologists has been suggested. In this paper, studies on image retrieval methods are reviewed with a special focus on quantification, utilization, and the evaluation of subjective similarities between pairs of images.

Journal

Radiological Physics and TechnologySpringer Journals

Published: May 8, 2018

References

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