Abstract
Presentation of images similar to a new unknown lesion can be helpful in medical image diagnosis and treatment planning. We have been investigating a method to retrieve relevant images as a diagnostic reference for breast masses on mammograms and ultrasound images. For retrieval of visually similar images, subjective similarities for pairs of masses were determined by experienced radiologists, and objective similarity measures were computed by modeling the subjective similarity space using multidimensional scaling (MDS). In this study, we investigated the similarity measure for masses on breast ultrasound images based on MDS and an artificial neural network and examined its usefulness in image retrieval. For 666 pairs of masses, correlation coefficient between the average subjective similarities and the MDS-based similarity measure was 0.724. When one to five images were retrieved, average precision in selecting relevant images, i.e., pathology-matched images for benign/malignant index image, was 0.778, indicating the potential utility of the proposed MDS-based similarity measure.
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Acknowledgements
Authors are grateful to Mikinao Oiwa, MD, PhD and Misaki Shiraiwa, MD, PhD for their contribution in this study. This study was supported in part by the Grant-in-Aid for Scientific Research for Young Scientists (no. 26860399) by Japan Society for the Promotion of Science and Grant-in-Aid for Scientific Research on Innovative Areas (no. 26108005) by Ministry of Education, Culture, Sports, Sciences and Technology in Japan.
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Muramatsu, C., Takahashi, T., Morita, T., Endo, T., Fujita, H. (2016). Similar Image Retrieval of Breast Masses on Ultrasonography Using Subjective Data and Multidimensional Scaling. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_6
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DOI: https://doi.org/10.1007/978-3-319-41546-8_6
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