Abstract
The purpose of this study was to investigate the usefulness of multiple-view mammograms in the computerized scheme for identifying histological classifications. Our database consisted of mediolateral oblique (MLO) and craniocaudal (CC) magnification mammograms obtained from 77 patients, which included 14 invasive carcinomas, 17 noninvasive carcinomas of comedo type, 17 noninvasive carcinomas of noncomedo type, 14 mastopathies, and 15 fibroadenomas. Five features on clustered microcalcifications were determined from each of MLO and CC images by taking into account image features that experienced radiologists commonly use to identify histological classifications. Modified Bayes discriminant function (MBDF) was employed for distinguishing between histological classifications. For the input of MBDF, we used five or ten features obtained from MLO and/or CC images. With ten features, the classification accuracies for each histological classification ranged from 70.6% to 93.3%. This result was higher than that obtained with only five features either from MLO or CC images.
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
Kopans, D.B.: Breast imaging, 2nd edn. Lippincott-Raven publishers, New York (1997)
Nakayama, R., Uchiyama, Y., Watanabe, R., Katsuragawa, S., Namba, K., Doi, K.: Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. Med. Phys. 31, 789–799 (2004)
Nakayama, R., Uchiyama, Y.: Development of new filter bank for detection of nodular patterns and line patterns in medical images. IEICE Trans. Inf. and Sys. J-87-D-II, 176–185 (2004)
Gonzales, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)
Sera, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1982)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Chichester (2001)
Kimura, F., Takahashi, K., Tsuruoka, S., Miyake, Y.: On avoiding peaking phenomenon of the quadratic discriminant function. IEICE J-69-D, 1328–1334 (1986)
Tsuruoka, S., Kurita, M., Harada, T., Kimura, F., Miyake, Y.: Handwriting ‘KANJI’ and ‘HIRAGANA’ character recognition using weighted direction index histogram method. IEICE J70-D, 1390–1397 (1987)
Tsuruoka, Morita, H., Kimura, F., Miyake, Y.: Handwritten character recognition adaptable to the writer. IEICE J-70-D, 1953–1960 (1987)
Aoyama, M., Li, Q., Katsuragawa, S., MacMahon, H., Doi, K.: Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. Med. Phys. 29, 701–708 (2002)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs (1992)
Langlotz, C.P.: Fundamental measures of diagnostic examination performance - Usefulness for clinical decision making and research. Radiology 228, 3–9 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nakayama, R. et al. (2006). Potential Usefulness of Multiple-Mammographic Views in Computer-Aided Diagnosis Scheme for Identifying Histological Classification of Clustered Microcalcification. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_32
Download citation
DOI: https://doi.org/10.1007/11783237_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
eBook Packages: Computer ScienceComputer Science (R0)