Name
Title | ||
|---|---|---|
Representation of lesion similarity by use of multidimensional scaling for breast masses on mammograms. |
Abstract | ||
|---|---|---|
Presentation of similar reference images can be useful for diagnosis of new lesions. A similarity map which can visually present the overview of the relationship between the lesions with different types may provide the supplemental information to the reference images. A new method for constructing the similarity map by multidimensional scaling (MDS) for breast masses on mammograms was investigated. Nine pathologic types were included; three regions of interests each from the nine groups were employed in this study. Subjective similarity ratings by expert readers were obtained for all possible 351 pairs of masses. Using the average ratings, MDS similarity map was created. Each axis of the MDS configuration was fitted by the linear model with 13 image features to reconstruct the similarity map. Dissimilarity based on the distance in the reconstructed space was determined and compared with the subjective rating. The MDS map consistently represented the similarity between cysts and fibroadenomas, invasive lobular carcinomas and scirrhous carcinomas, and ductal carcinomas in situ, solid-tubular carcinomas, and papillotubular carcinomas with the experts' data. The correlation between the average subjective ratings and the dissimilarities based on the distance in the reconstructed feature space was much greater (-0.87) than that of the dissimilarities based on the distance in the conventional feature space (-0.65). The new similarity map by MDS can be useful for visualizing the relationship between breast masses with different pathologic types. It has potential usefulness in selecting the similarity measures and providing the supplemental information. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10278-012-9569-0 | J. Digital Imaging |
Keywords | Field | DocType |
Similarity map, Multidimensional scaling, Breast masses, Digital mammography | Digital mammography,Mammography,Computer vision,Scirrhous Carcinomas,Data mining,Feature vector,Multidimensional scaling,Computer science,Linear model,Feature (computer vision),Correlation,Artificial intelligence | Journal |
Volume | Issue | ISSN |
26 | 4 | 1618-727X |
Citations | PageRank | References |
3 | 0.56 | 11 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chisako Muramatsu | 1 | 317 | 35.56 |
| Kohei NIshimura | 2 | 17 | 4.37 |
| Tokiko Endo | 3 | 3 | 0.56 |
| Mikinao Oiwa | 4 | 8 | 3.11 |
| Misaki Shiraiwa | 5 | 7 | 2.38 |
| Kunio Doi | 6 | 3 | 0.56 |
| Hiroshi Fujita | 7 | 3 | 1.24 |