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Evaluation of Objective Similarity Measures for Selecting Similar Images of Mammographic Lesions

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Abstract

The purpose of this study was to investigate four objective similarity measures as an image retrieval tool for selecting lesions similar to unknown lesions on mammograms. Measures A and B were based on the Euclidean distance in feature space and the psychophysical similarity measure, respectively. Measure C was the sequential combination of B and A, whereas measure D was the sequential combination of A and B. In this study, we selected 100 lesions each for masses and clustered microcalcifications randomly from our database, and we selected five pairs of lesions from 4,950 pairs based on all combinations of the 100 lesions by use of each measure. In two observer studies for 20 mass pairs and 20 calcification pairs, six radiologists compared all combinations of 20 pairs by using a two-alternative forced-choice method to determine the subjective similarity ranking score which was obtained from the frequency with which a pair was considered as more similar than the other 19 pairs. In both mass and calcification pairs, pairs selected by use of measure D had the highest mean value of the average subjective similarity ranking scores. The difference between measures D and A (P = 0.008 and 0.024), as well as that between measures D and B (P = 0.018 and 0.028) were statistically significant for masses and microcalcifications, respectively. The sequential combination of the objective similarity measure based on the Euclidean distance and the psychophysical similarity measure would be useful in the selection of images similar to those of unknown lesions.

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References

  1. Doi K: Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 78:s3–s19, 2005

    Article  PubMed  Google Scholar 

  2. Doi K: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31:198–211, 2007

    Article  PubMed  Google Scholar 

  3. Doi K: Computer-aided diagnosis moves from breast to other systems. Diagn Imag 29:37–40, 2007

    Google Scholar 

  4. Kumazawa S, Muramatsu C, Li Q, et al: An investigation of radiologists’ perception of lesion similarity: observations with paired breast masses on mammograms and paired lung nodules on CT images. Acad Radiol 15:887–894, 2008

    Article  PubMed  Google Scholar 

  5. Bucci G, Cagnoni S, De Dominicis R: Integrating content-based retrieval in a medical image reference database. Comput Med Imaging Graph 20:231–241, 1996

    Article  CAS  PubMed  Google Scholar 

  6. Wong ST, Huang HK: Design methods and architectural issues of integrated medical image data base systems. Comput Med Imaging Graph 20:285–299, 1996

    Article  CAS  PubMed  Google Scholar 

  7. Swett HA, Fisher PR, Cohn AI, et al: Expert system controlled image display. Radiology 172:487–493, 1989

    CAS  PubMed  Google Scholar 

  8. Swett HA, Mutalik PG, Neklesa VP, et al: Voice-activated retrieval of mammography reference images. J Digit Imaging 11:65–73, 1998

    Article  CAS  PubMed  Google Scholar 

  9. Qi H, Snyder WE: Content-based image retrieval in picture archiving and communications systems. J Digit Imaging 12(2 Suppl 1):81–83, 1999

    Article  CAS  PubMed  Google Scholar 

  10. Sklansky J, Tao EY, Bazargan M, et al: Computer-aided, case-based diagnosis of mammographic regions of interest containing microcalcifications. Acad Radiol 7:395–405, 2000

    Article  CAS  PubMed  Google Scholar 

  11. Giger ML, Huo Z, Vyborny CJ, et al: Intelligent CAD workstation for breast imaging using similarity to known lesions and multiple visual prompt aids. Proc SPIE 4684:768–773, 2002

    Article  Google Scholar 

  12. Sinha U, Kangarloo H: Principal component analysis for content-based image retrieval. Radiographics 22:1271–1289, 2002

    PubMed  Google Scholar 

  13. Aisen AM, Broderick LS, Winer-Muram H, et al: Automated storage and retrieval of thin-section CT images to assist diagnosis: system description and preliminary assessment. Radiology 228:265–270, 2003

    Article  PubMed  Google Scholar 

  14. Kawata Y, Niki N, Ohmatsu H, et al: Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images. Acad Radiol 10:1402–1415, 2003

    Article  PubMed  Google Scholar 

  15. El-Naqa I, Yang Y, Galatsanos NP, et al: A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging 23:1233–1244, 2004

    Article  PubMed  Google Scholar 

  16. Zheng B, Lu A, Hardesty LA, et al: A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment. Med Phys 33:111–117, 2006

    Article  PubMed  Google Scholar 

  17. Nakayama R, Watanabe R, Namba K, et al: An improved computer-aided diagnosis scheme using the nearest neighbor criterion for determining histological classification of clustered microcalcifications. Methods Inf Med 46:716–722, 2007

    CAS  PubMed  Google Scholar 

  18. Li Q, Li F, Shiraishi J, et al: Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules. Med Phys 30:2584–2593, 2003

    Article  PubMed  Google Scholar 

  19. Muramatsu C, Li Q, Suzuki K, et al: Investigation of psychophysical measure for evaluation of similar images for mammographic masses: preliminary results. Med Phys 32:2295–2304, 2005

    Article  PubMed  Google Scholar 

  20. Muramatsu C, Li Q, Schmidt RA, et al: Experimental determination of subjective similarity for pairs of clustered microcalcifications on mammograms: observer study results. Med Phys 33:3460–3468, 2006

    Article  PubMed  Google Scholar 

  21. Muramatsu C, Li Q, Schmidt RA, et al: Determination of subjective similarity for pairs of masses and pairs of clustered microcalcifications on mammograms: comparison of similarity ranking scores and absolute similarity ratings. Med Phys 34:2890–2895, 2007

    Article  PubMed  Google Scholar 

  22. Muramatsu C, Li Q, Schmidt RA, et al: Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms. Med Phys 35:5695–5702, 2008

    Article  PubMed  Google Scholar 

  23. Muramatsu C: Investigation of similarity measures for selection of similar images in computer-aided diagnosis of breast lesions on mammograms. Ph.D. dissertation, The University of Chicago, Chicago, IL (ProQuest/UMI, Ann Arbor, MI, 2008)

  24. Kendall M, Gibbons JD: Rank correlation methods, 5th edition. Oxford University Press, New York, 1990

    Google Scholar 

  25. Heath M, Bowyer K, Kopans D, et al: Current status of the digital database for screening mammography. In: Digital mammography. Dordrecht: Kluwer Academic, 1998

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Acknowledgments

We are grateful to Kirti Kulkarni, MD, Charlene Sennett, MD, Rajshri Shah, MD, Steven G. Thiel, MD, and Akiko Shimauchi, MD, for participating in the observer study; to Chisako Muramatsu, PhD, for her useful suggestions; and to Elisabeth Lanzl for improving the manuscript. K. Doi is a shareholder in Hologic/R2 Technology, Inc., Los Altos, CA, USA. CAD technologies developed in the Kurt Rossmann Laboratories have been licensed to R2 Technology, Deus Technologies, Riverain Medical Group, Mitsubishi Space Software Co., Median Technologies, General Electric Corporation, and Toshiba Corporation. It is the policy of The University of Chicago that investigators disclose publicly actual or potential significant financial interests that may appear to affect research activities or that may benefit from research activities.

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Correspondence to Ryohei Nakayama.

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Nakayama, R., Abe, H., Shiraishi, J. et al. Evaluation of Objective Similarity Measures for Selecting Similar Images of Mammographic Lesions. J Digit Imaging 24, 75–85 (2011). https://doi.org/10.1007/s10278-010-9288-3

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