Breast tissue classification in digital breast tomosynthesis images using texture features: A feasibility study

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7260, Issue , 2009, Pages

  Kontos, Despina ^a   Berger, Rachelle ^a   Bakic, Predrag R ^a   Maidment, Andrew D A ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Breast; Classifier design; Dense tissue classification; Digital breast tomosynthesis; Texture analysis

Indexed keywords

BREAST; CLASSIFIER DESIGN; DENSE TISSUE CLASSIFICATION; DIGITAL BREAST TOMOSYNTHESIS; TEXTURE ANALYSIS;

CLASSIFIERS; COMPUTER OPERATING PROCEDURES; ESTIMATION; FRACTAL DIMENSION; LEARNING SYSTEMS; MAMMOGRAPHY; MAXIMUM LIKELIHOOD ESTIMATION; MEDICAL IMAGING; PLANNING; RESOURCE ALLOCATION; RISK PERCEPTION; TEXTURES; THREE DIMENSIONAL; TISSUE;

COMPUTER AIDED DIAGNOSIS;

EID: 66749161546     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.813812     Document Type: Conference Paper

References (20)

1. Boyd, N.F., Guo, H., Martin, L.J, et al., "Mammographic density and the risk and detection of breast cancer," New England Journal of Medicine 356(3), 227-236 (2007). (Pubitemid 46143226)
2. Karssemeijer, N., "Automated classification of parenchymal patterns in mammograms," Physics in Medicine & Biology 43(2), 365-378 (1998). (Pubitemid 28100138)
3. Zhou, C., Chan, H.P., Petrick, N., et al., "Computerized image analysis: estimation of breast density on mammograms," Medical Physics 28(6), 1056-1069 (2001). (Pubitemid 32574690)
4. Oliver, A., Freixenet, J., Mart, R., et al., "A novel breast tissue density classification methodology," IEEE Transactions on Information Technology in Biomedicine 12(1), 55-65 (2008).
5. Kopans, D.B., "Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk," Radiology 246(2), 348-353 (2008).
6. Yaffe, M.J., "Mammographic density. Measurement of mammographic density," Breast Cancer Research 10(3), 209-219 (2008).
7. Park, J.M., Franken, E.A., Jr., Garg, M., Fajardo, L.L., Niklason, L.T., "Breast tomosynthesis: present considerations and future applications," Radiographics 27(Suppl 1), S231-240 (2007).
8. Kontos, D., Bakic, P.R., Troxel, A.B., Conant, E.F., Maidment, A.D.A., "Digital breast tomosynthesis parenchymal texture analysis for breast cancer risk estimation: A preliminary study," In: E. A. Krupinski (ed.), Digital Mammography (IWDM), Springer Lectures Notes in Computer Science (LNCS) 5116, 491-498 (2008).
9. Duda, R., Hart, P., Stork, D., Pattern Classification, John Wiley and Sons (2000).
10. Li, H., Giger, M.L., Olopade, O.I., Margolis, A., Lan, L., Chinander, M.R., "Computerized Texture Analysis of Mammographic Parenchymal Patterns of Digitized Mammograms," Academic Radiology 12, 863-873 (2005). (Pubitemid 41008679)
11. Amadasum, M., King, R., "Textural features corresponding to textural properties," IEEE Transactions on Systems Man and Cybernetics 19, 1264-1274 (1989). (Pubitemid 20642913)
12. Haralick, R.M., Shanmugam, K., Dinstein, I., "Textural features for image classification," IEEE Transactions on Systems, Man and Cybernetics 3, 610-621 (1973).
13. Kontos, D., Bakic, P.R., Maidment, A.D.A., "Texture in digital breast tomosynthesis: A comparison between mammographic and tomographic characterization of parenchymal properties," In Proc. of SPIE Medical Imaging: Computer Aided Diagnosis (6915), San Diego, CA (2008).
14. Russ, J.C., Fractal Surfaces, New York Plenum Press (1994).
15. Li, H., Giger, M.L., Olopade, O.I., Chinander, M.R., "Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment," Journal of Digital Imaging 21(2), 145-152 (2008).
16. Chen, W., Giger, M.L., Li, H., Bick, U., Newstead, G.M., "Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images," Magnetic Resonance in Medicine 58(3), 562-571 (2007). (Pubitemid 350154967)
17. Kurt Rossmann Laboratories for Radiologic Image Research (*http://xray.bsd.uchicago.edu/krl/).
18. Medical Image Perception Laboratory (*http://perception.radiology. uiowa.edu).
19. Pesce, L.L., Metz, C.E., "Reliable and computationally efficient maximum-likelihood estimation of "proper" binormal ROC curves," Academic Radiology 14(7), 814-829 (2007). (Pubitemid 46898850)
20. Metz, C.E., Pan, X., " "Proper" Binormal ROC Curves: Theory and Maximum-Likelihood Estimation," Journal of Mathematical Psychology 43(1), 1-33 (1999).