Abstract
Purpose
Breast parenchymal density is an important risk factor for breast cancer. It is known that mammogram interpretation is more difficult where dense tissue is involved. Therefore, automated breast density classification may aid in breast lesion detection and analysis.
Methods
Several image pattern classification techniques for screen-film (SFM) mammography datasets were tested and classified according to BIRADS categories using known cases. A hierarchical classification procedure based on k-NN, SVM and LBN combined with principal component analysis on texture features uses the breast density features. The classification techniques have been incorporated into a CADe system to drive the detection algorithms.
Results
The results obtained on 322 mammograms demonstrate that up to 84% of samples were correctly classified. The results of the lesion detection algorithms were obtained from modules integrated within the CADe system developed by the authors.
Conclusions
The ability to detect suspicious lesions on dense and heterogeneous tissue has been tested. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints.
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References
Bueno G (2008) In: Fuzzy systems and deformable models. Series in medical physics and biomedical engineering. Taylor and Francis group, London, pp 305–329 (Intelligent and Adaptive Systems in Medicine)
Boyd N, Dite G, Stone J et al (2002) Reliability of mammographic density, a risk factor for breast cancer. New Engl J Med 347(12): 886–894
Ursin G, Hovanessian-Larsen L, Parisky YR et al (2005) Greatly increased occurrence of breast cancers in areas of mammographically dense tissue. Breast Cancer Res 7(5): 605–608
Brem R, Hoffmeister J, Rapelyea J et al (2005) Impact of breast density on computer-aided detection for breast cancer. Am J Roentgenol 184: 439–444
Wolfe JN (1976) Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37: 2486–2492
Oliver A, Freixenet J, Martí R et al (2008) A novel breast tissue density classification methodology. IEEE Trans Info Tech Biomed 12: 55–65
Yafee M, Boyd N (2005) Mammographic breast density and cancer risk: the radiological view. Gynecol Endocrinol 21(Supplement 1): 6–11
Koutras A, Christoyianni I, Georgoulas G, Dermatas E (2006) Computer aided classification of mammographic tissue using independent component analysis and support vector machines. Lect Notes Comput Sci 4132(1): 568–577
Gorgel P, Sertbas A, Kilic N, Ucan O, Osman O (2009) Mammographic mass classification using wavelet based support vector machine. J Electr Electron Eng 9(1): 867–875
Chang R, Wu W, Moon WK, Chou Y, Chen D (2003) Support vector machines for diagnosis of breast tumors on us images. Acad Radiol 10(2): 189–197
Mavroforakis M, Georgios H, Dimitropoulos N, Cavouras D, Theodoridis S (2006) Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artif Intell Med 37(2): 145–162
Fu JC, Lee SK, Wong STC, Yeh JY, Wang AH, Wu HK (2005) Image segmentation feature selection and pattern classification for mammographic microcalcifications. Comput Med Imaging Graph 29: 419–429
Christoyianni I, Koutras A, Dermatas E, Kokkinakis G (2001) Breast tissue classification in mammograms using ica mixture models. Lect Notes Comput Sci 2130(1): 554–560
American College of Radiology (2003) Breast imaging reporting and data system atlas (BIRADS). ACR, Reston, Va
Bovis K, Singh S (2002) Classification of mammographic breast density using a combined classifier paradigm. In: 4th international workshop on digital mammography, pp 177–180
Bosch A, Munoz X, Oliver A, Marti J (2006) Modeling and classifying breast tissue density in mammograms. In: Proceedings IEEE computer society conference on computer vision and pattern recognition, vol 21, pp 1552–1558
Oliver A, Lladó X, Martí R, Freixenet J, Zwiggelaar R (2007) Classifying mammograms using texture information. In: Proceedings medical image understanding and analysis, pp 223–227
Haralick R, Sternberg S, Zhuang X (1987) Image analysis using mathematical morphology. IEEE Trans Pattern Anal Mach Intell 9(4): 532–550
Kuncheva Ludmila I (2004) Combining pattern classifiers. Wiley, New York
Duda RO, Hart PE, Stork DG (2001) Pattern Classification. Wiley, New York
Bueno G, Ruiz M, Sánchez S (2006) B-spline filtering for automatic detection of calcification lesions in mammograms. In: Proceedings of the Intern. conference on information optics, WIO’06. pp 60–70
Petroudi S, Kadir T, Brady M (2003) Automatic classification of mammographic parenchymal patterns: a statistical approach. In: Proceedings IEEE conference engineering medicine Biology Society vol 1, pp 798–801
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Bueno, G., Vállez, N., Déniz, O. et al. Automatic breast parenchymal density classification integrated into a CADe system. Int J CARS 6, 309–318 (2011). https://doi.org/10.1007/s11548-010-0510-z
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DOI: https://doi.org/10.1007/s11548-010-0510-z