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Proceedings Paper

Image retrieval of breast masses on ultrasound images
Author(s): Chisako Muramatsu; Shunichi Higuchi; Takako Morita; Mikinao Oiwa; Tomonori Kawasaki; Hiroshi Fujita
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Paper Abstract

Presentation of images similar to a new unknown lesion as a reference can be helpful in medical image diagnosis and treatment planning. We have been investigating a method to determine similarity of breast masses as an image retrieval index for an intelligent image analytic system that may support radiologists’ efficient image interpretation. In order to retrieve perceptually similar images, we have obtained subjective similarity ratings from expert radiologists, which were then used in similarity space modeling and training deep neural networks. In this study, we investigated the use of convolutional neural network to model the similarity space for retrieval of diagnostically relevant reference images and also to directly estimate similarity ratings for pairs of images. The preliminary results show that retrieval performance was slightly better in similarity space modeling method than direct estimation method. These results indicate the potential usefulness of the proposed methods for retrieval of reference images as diagnostic assistance.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 1095517 (15 March 2019); doi: 10.1117/12.2513663
Show Author Affiliations
Chisako Muramatsu, Gifu Univ. (Japan)
Shunichi Higuchi, Gifu Univ. (Japan)
Takako Morita, Nagoya Medical Ctr. (Japan)
Mikinao Oiwa, Nagoya Medical Ctr. (Japan)
Tomonori Kawasaki, Saitama Medical Univ. (Japan)
Hiroshi Fujita, Gifu Univ. (Japan)

Published in SPIE Proceedings Vol. 10955:
Medical Imaging 2019: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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