Name
Title | ||
|---|---|---|
Psychophysical similarity measure based on multi-dimensional scaling for retrieval of similar images of breast masses on mammograms |
Abstract | ||
|---|---|---|
For retrieving reference images which may be useful to radiologists in their diagnosis, it is necessary to determine a reliable similarity measure which would agree with radiologists' subjective impression. In this study, we propose a new similarity measure for retrieval of similar images, which may assist radiologists in the distinction between benign and malignant masses on mammograms, and investigated its usefulness. In our previous study, to take into account the subjective impression, the psychophysical similarity measure was determined by use of an artificial neural network (ANN), which was employed to learn the relationship between radiologists' subjective similarity ratings and image features. In this study, we propose a psychophysical similarity measure based on multi-dimensional scaling (MDS) in order to improve the accuracy in retrieval of similar images. Twenty-seven images of masses, 3 each from 9 different pathologic groups, were selected, and the subjective similarity ratings for all possible 351 pairs were determined by 8 expert physicians. MDS was applied using the average subjective ratings, and the relationship between each output axis and image features was modeled by the ANN. The MDS-based psychophysical measures were determined by the distance in the modeled space. With a leave-one-out test method, the conventional psychophysical similarity measure was moderately correlated with subjective similarity ratings (r=0.68), whereas the psychophysical measure based on MDS was highly correlated (r=0.81). The result indicates that a psychophysical similarity measure based on MDS would be useful in the retrieval of similar images. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2001037 | Proceedings of SPIE |
Keywords | Field | DocType |
psychophysical similarity measure,multi-dimensional scaling,similar image,breast masses,mammograms | Mammography,Computer vision,Pattern recognition,Similarity measure,Multidimensional scaling,Feature (computer vision),Artificial intelligence,Artificial neural network,Scaling,Physics | Conference |
Volume | ISSN | Citations |
8670 | 0277-786X | 1 |
PageRank | References | Authors |
0.38 | 1 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kohei NIshimura | 1 | 17 | 4.37 |
| Chisako Muramatsu | 2 | 317 | 35.56 |
| Mikinao Oiwa | 3 | 8 | 3.11 |
| Misaki Shiraiwa | 4 | 7 | 2.38 |
| Tokiko Endo | 5 | 56 | 8.59 |
| kunio doi | 6 | 4 | 1.48 |
| Hiroshi Fujita | 7 | 86 | 19.92 |