Abstract
It is often difficult for clinicians to decide correctly on either biopsy or follow-up for breast lesions with masses on ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of breast mass by using objective features corresponding to clinicians’ subjective impressions for image features on ultrasonographic images. Our database consisted of 363 breast ultrasonographic images obtained from 363 patients. It included 150 malignant (103 invasive and 47 noninvasive carcinomas) and 213 benign masses (87 cysts and 126 fibroadenomas). We divided our database into 65 images (28 malignant and 37 benign masses) for training set and 298 images (122 malignant and 176 benign masses) for test set. An observer study was first conducted to obtain clinicians’ subjective impression for nine image features on mass. In the proposed method, location and area of the mass were determined by an experienced clinician. We defined some feature extraction methods for each of nine image features. For each image feature, we selected the feature extraction method with the highest correlation coefficient between the objective features and the average clinicians’ subjective impressions. We employed multiple discriminant analysis with the nine objective features for determining histological classification of mass. The classification accuracies of the proposed method were 88.4 % (76/86) for invasive carcinomas, 80.6 % (29/36) for noninvasive carcinomas, 86.0 % (92/107) for fibroadenomas, and 84.1 % (58/69) for cysts, respectively. The proposed method would be useful in the differential diagnosis of breast masses on ultrasonographic images as diagnosis aid.
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Berg WA, Blume JD, Cormack JB, et al: Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 299:2151–2163, 2008
Berg WA: Rationale for a trial of screening breast ultrasound: American College of Radiology Imaging Network (ACRIN) 6666. Am J Roentgenol 181:1426–1428, 2003
Kaplan SS: Clinical utility of bilateral whole-breast US in the evaluation of women with dense breast tissue. Radiology 221:641–649, 2001
Kolb TM, Lichy J, Newhouse JH: Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them. An analysis of 27, 825 patient evaluations. Radiology 225:165–175, 2002
Doi K, MacMahon H, Katsuragawa S, Nishikawa RM, Jiang Y: Computer-aided diagnosis in radiology: potential and pitfall. Eur J Radiol 31:97–109, 1999
Chen DR, Chang RF, Huang YL: Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. Radiology 213:407–412, 1999
Chen DR, Huang YL, Lin SH: Computer-aided diagnosis with textural features for breast lesions in sonograms. Comput Med Imaging Graph 35:220–226, 2011
Joo S, Yang YS, Moon WK, Kim HC: Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Trans Med Imaging 23:1292–1300, 2004
Shi X, Cheng HD, Hu L, Ju W, Tian J: Detection and classification of masses in breast ultrasound images. Digit Signal Process 20:824–836, 2010
Horsch K, Giger ML, Venta LA, Vybomy CJ: Computerized diagnosis of breast lesions on ultrasound. Med Phys 29:157–164, 2002
Kopans DB: Breast Imaging, 2nd edition. Lippincott-Raven, New York, 1997
Morimoto T, Sasa M Eds: Atlas of Screening Mammography. Digital Press, Tokyo, 1996
Sakamoto G, Haga S: Fundamental and Clinic of Ductal Carcinoma In Situ. Shinoharashinsha, Tokyo, 2001
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
Chen CM, Chou YH, Han KC, Hung CS, Tiu CM, Chiou HJ, Chiou SY: Breast lesions on sonograms: computer-aided diagnosis with nearly setting independent features and artificial neural networks. Radiology 226:504–514, 2003
Takemura A, Shimizu A, Hamamoto K: Discrimination of breast tumors in ultrasonic images by classifier ensemble trained with adaboost. IEEJ 129:620–629, 2009 (in Japanese)
Takemura A, Shimizu A, Hamamoto K: Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the adaboost algorithm with features selection. IEEE Tran Med Imaging 29:598–609, 2010
Huo Z, Giger ML, Vyborny CJ, Bick U, Lu P, Wolverton DE, Schmidt RA: Analysis of spiculation in the computerized classification of mammographic masses. Med Phys 22:1569–1579, 1995
Cheng HD, Shan J, Ju W, Guo Y, Zhang L: Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognit 43:229–317, 2010
Giger M, Yuan Y, Li H, Drukker K, Chen W, Lan L, Ho K: Progress in breast CADx. In: Biomedical Imaging. Fourth IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007, pp. 508–511
Shen WC, Chang RF, Moon WK: Computer aided classification system for breast ultrasound based on breast imaging reporting and data system (BI-RADS). Ultrasound Med Biol 33:1688–1698, 2007
Ballard DH: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit 13:111–122, 1981
Sklansky J: Measuring concavity on a rectangular mosaic. IEEE Trans Comput 21:1355–1364, 1972
Muramatsu C, Li Q, Schmidt RA, 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, Schmidt RA, 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
Duda RO, Hart PE, Stork DG: Pattern Classification. Wiley, New York, 2001, pp 282–349
Kuncheva LI: Combining Pattern Classifiers: Methods and Algorithms. New York: Wiley, 2004
Langlotz CP: Fundamental measures of diagnostic examination performance: Usefulness for clinical decision making and research. Radiology 228:3–9, 2003
Johnson RA, Wichern DW: Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs, 1992
Nakayama R, Kashikura Y, Namba K, Kobayashi S, Takeda K, Ogawa T, Hizukuri A: “Computer-aided Diagnosis Scheme for Determing Histological Classifications of Breast Masses on Ultrasonographic Image,” Radiological Society of North America 2010 95th Scientific Assembly and Annual Metting (RSNA2010). Chicago, Dec. 2010
Hizukuri A, Nakayama R, Kashikura Y, Nakako N, Kawanaka H, Takase H, Ogawa T, Tsuruoka S: Computer-aided diagnosis scheme for histological classification of breast mass on ultrasonographic images. IEICE Tech Rep 47:153–158, 2011. Japanese
Ashizawa K, MacMahon H, Ishida T, Nakamura K, Vyborny CJ, Katsuragawa S, Doi K: Effect of artificial neural network on radiologists’ performance for differential diagnosis of interstitial lung disease on chest radiographs. Am J Radiol 17:1311–1314, 1999
Jiang Y, Nishikawa RM, Schmidt RA, Metz CE, Giger ML, Doi K: Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol 6:22–33, 1999
Acknowledgments
We are grateful to Masahiro Nakai, MD, at Mie Prefectural Association for Health Care Center and, Masako Yamashita, MD, at Mie University hospital, for their participation in the observer study conducted in this study and valuable suggestions.
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Hizukuri, A., Nakayama, R., Kashikura, Y. et al. Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images. J Digit Imaging 26, 958–970 (2013). https://doi.org/10.1007/s10278-013-9594-7
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DOI: https://doi.org/10.1007/s10278-013-9594-7