Retrieval of reference images of breast masses on mammograms by similarity space modeling

Chisako Muramatsu; Shunichi Higuchi; Takako Morita; Mikinao Oiwa; Tomonori Kawasaki; Hiroshi Fujita

doi:10.1117/12.2318717

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6 July 2018 Retrieval of reference images of breast masses on mammograms by similarity space modeling

Chisako Muramatsu, Shunichi Higuchi, Takako Morita, Mikinao Oiwa, Tomonori Kawasaki, Hiroshi Fujita

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Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 1071809 (2018) https://doi.org/10.1117/12.2318717
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

Abstract

Presentation of reference images that are similar to a query image can be helpful in medical image diagnosis and treatment planning. The purpose of this study is to investigate a method for retrieving relevant images of breast masses on mammograms as a diagnostic reference. In our previous studies, subjective similarities for pairs of masses were obtained from experienced radiologists and used as the gold standard for retrieving visually similar images. By use of multidimensional scaling, a subjective similarity space was spanned so that masses that were placed close to a query image can be retrieved as reference images. This method, however, required manual outlines of masses for image feature determination. In this study, we modelled this similarity space using convolutional neural network. The result was evaluated using the leave-one-out cross validation method in terms of the correlation between the subjective ratings and determined similarity measures. The relevance of retrieved images was also evaluated in terms of the precision, which is the fraction of pathology matched images in the retrieved images. The correlation coefficient between the subjective ratings and the determined similarity measure was moderate with 0.735, which was slightly lower than those of the previous methods. The average precision was high with 0.852 when the most similar image was retrieved, which was higher than those in the previous studies. The results indicate the potential usefulness of the proposed method in similar image retrieval of breast masses on mammograms.

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Chisako Muramatsu, Shunichi Higuchi, Takako Morita, Mikinao Oiwa, Tomonori Kawasaki, and Hiroshi Fujita "Retrieval of reference images of breast masses on mammograms by similarity space modeling", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 1071809 (6 July 2018); https://doi.org/10.1117/12.2318717

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