Abstract
In multi-modality, multi-information breast cancer diagnosis framework, radiologists take into account all the information available in making diagnosis, one of which can be the information from reference cases. The purpose of this study is to investigate the relationship between pathological concordance and image similarity of breast masses for exploring the utility of similar images and determining the effective similarity index for image retrieval. Twenty-seven images of masses, three from each of 9 pathologic types, were used in this study. Subjective similarity ratings for all possible pairs (351 pairs) were provided by 8 expert readers. Thirteen image features were determined, and their usefulness as a similarity index was examined. Generally, masses with the same pathologic types were considered more similar (0.75) than those with different types (0.43) by the experts, although cysts and fibroadenomas appeared very similar on mammograms. Perimeter, ellipticity, radial gradient index, and full-width at half maximum of radial gradient histogram were considered potentially useful (correlation, r>0.4) for estimating subjective similarity among image features. Similar images together with their clinical data may serve as a useful reference for diagnosis of breast lesions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tabar, L., Fagerberg, G., Duffy, S.W., Day, N.E., Gad, A., Grontoft, O.: Update of the Swedish two-county program of mammographic screening for breast cancer. Radiol. Clin. North Am. 30, 187–210 (1992)
Shapiro, S., Venet, W., Strax, P., Venet, L., Roeser, R.: Selection, follow-up, and analysis in the health insurance plan study: A randomized trial with breast cancer screening. J. Natl. Cancer Inst. Monogr. 67, 65–74 (1985)
Humphrey, L.L., Helfand, M., Chan, B.K.S., Woolf, S.H.: Breast cancer screening: A summary of the evidence for the U.S. preventive services task force. Annals. of Internal Medecine 137, E-347-3-367 (2002)
Chan, H.P., Sahiner, B., Roubidoux, M.A., Wilson, T.E., Adler, D.D., Paramagul, C., Newman, J.S., Sanjay-Gopal, S.: Improvement of radiologists‘ characterization of mammographic masses by using computer-aided diagnosis: An ROC study. Radiology 212, 817–827 (1999)
Huo, Z., Giger, M.L., Vyborny, C.J., Metz, C.E.: Breast cancer: Effectiveness of computer-aided diagnosis - observer study with independent database of mammograms. Radiology 224, 560–568 (2002)
Jiang, Y., Nishikawa, R.M., Schmidt, R.A., Metz, C.E., Giger, M.L., Doi, K.: Improving breast cancer diagnosis with computer-aided diagnosis. Acad. Radiol. 6, 22–33 (1999)
Swett, H.A., Fisher, P.R., Cohn, A.I., Miller, P.L., Mutalik, P.G.: Expert system-controlled image display. Radiology 172, 487–493 (1989)
Qi, H., Snyder, W.E.: Cotent-based image retrieval in picture archiving and communications systems. J. Digit. Imaging 12, 81–83 (1999)
Sklansky, J., Tao, E.Y., Bazargan, M., Ornes, C.J., Murchison, R.C., Teklehaimanot, S.: Computer-aided, case-based diagnosis of mammographic regions of interest containing microcalcifications. Acad. Radiol. 7, 395–405 (2000)
Giger, M.L., Huo, Z., Vyborny, C.J., Lan, L., Bonta, I., Horsch, K., Nishikawa, R.M., Rosenbourgh, I.: Interlligent CAD workstation for breast imaging using similarity to known lesions and multiple visual prompt aids. In: Proc. SPIE Medical Imaging, vol. 4684, pp. 768–773 (2002)
Aisen, A.M., Broderick, L.S., Winer-Muram, H., Brodley, C.E., Kak, A.C., Pavlopoulou, C., Dy, J., Shyu, C.R., Marchiori, A.: Automated storage and retrieval of thin-section CT images to assist diagnosis: System description and prekliminary assessment. Radiology 228, 265–270 (2003)
Li, Q., Li, F., Shiraishi, J., Katsuragawa, S., Sone, S., Doi, K.: Investigation of new psychophysical measures for evaluaation of similar images on thoracic CT for distinction between benign and malignant nodules. Med. Phys. 30, 2584–2593 (2003)
Nishikawa, R.M., Yang, Y., Huo, D., Wernick, M., Sennett, C.A., Papioannou, J., Wei, L.: Observers‘ ability to judge the similarity of clustered calcifications on mammograms. In: Proc. SPIE Medical Imaging, vol. 5371, pp. 192–198 (2004)
Muramatsu, C., Li, Q., Suzuki, K., Schmidt, R.A., Shiraishi, J., Newstead, G.M., Doi, K.: Investigation of psychophysical measure for evaluation of similar images for mammographic masses: Preliminary results. Med. Phys. 32, 2295–2304 (2005)
Muramatsu, C., Li, Q., Schmidt, R.A., Shiraishi, J., Doi, K.: Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms. Med. Phys. 35, 5695–5702 (2008)
Muramatsu, C., Li, Q., Schmidt, R.A., Shiraishi, J., Doi, K.: Determination of similarity measures for pairs of mass lesions on mammograms by use of BI-RADS lesion descriptors and image features. Acad. Radiol. 16, 443–449 (2009)
Muramatsu, C., Schmidt, R.A., Shiraishi, J., Li, Q., Doi, K.: Presentation of similar images as a reference for distinction between benign and malignant masses on mammograms: Analysis of initial observer study. J. Digit. Imaging 23, 592–602 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muramatsu, C. et al. (2012). Correspondence among Subjective and Objective Similarities and Pathologic Types of Breast Masses on Digital Mammography. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-31271-7_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31270-0
Online ISBN: 978-3-642-31271-7
eBook Packages: Computer ScienceComputer Science (R0)