In 2D ultrasound Computer-Aided Diagnosis (CAD), the main emphasis is extraction of tumor boundaries and its classification into benign and malignant types. This provides a direct tool for breast radiologists and can even prevent breast biopsies, thereby reducing the number of false positives. The prerequisite for accurate breast boundary estimation in 2D breast ultrasound images is accurate segmentation of breast tumors and shape modeling. But this is a challenging task, because there is no set pattern of progression of tumors in the spatiotemporal domain. This chapter adapts a methodology based on geometric deformable models such as the level set, which has the ability to extract the topology of shapes of breast tumors. Using this framework, we extract several features of breast tumors and feed this set of information into a vector machine-based classifier for classification of breast disease. Our system demonstrates accuracy, sensitivity, specificity, PPV, and NPV values of 87, 85, 88, 82, and 89%, respectively.
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7 References
American Cancer Society. 2004. Cancer facts and figures 2004. Arlington, VA: American Cancer Society.
Ganott MA, Harris KM, Klaman HM, Keeling TL. 1999. Analysis of false-negative cancer cases identified with a mammography audit. Breast J. 5(3):166-175.
Crystal P, SD Strano, Shcharynski S, Koretz MJ. 2003. Using sonography to screen women with mammographically dense breasts. AJR 181(1):177-182.
Bassett LW, Ysrael M, Gold RH, Ysrael C. 1991. Usefulness of mammography and sonography in women less than 35 years of age. Radiology 180(3):831-835.
Jackson VP. 1990. The role of the US in breast imaging. Radiology 177(2):305-311.
Stavros AT, Thickman D, Rapp CL, Dennis MA, Parker SH, Sisney GA. 1995. Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology 196(no. 1):123-134.
Drukker K, Giger ML, Horsch K, Kupinski MA, Vyborny CJ, Mendelson EB. 2002. Comput- erized lesion detection on breast ultrasound. Med Phys 29(7):1438-1446.
Horsch K, Giger ML, Venta LA, Vyborny CJ. 2002. Computerized diagnosis of breast lesions on ultrasound. Med Phys 29(2):157-164.
Chang RF, Kuo WJ, Chen DR, Huang YL, Lee JH, Chou YH. 2000. Computer-aided diagnosis for surgical office-based breast ultrasound. Arch Surg 135(6):696-699.
Kuo WJ, Chang RF, Moon WK, Lee CC, Chen DR. 2002. Computer-aided diagnosis of breast tumors with different US systems. Acad Radiol 9(7):793-799.
Chen DR, Chang RF, Chen WM, Moon WK. 2003. Computer-aided diagnosis for 3-dimensional breast ultrasonography. Arch Surg 138(3):296-302.
Chen DR, Kuo WJ, Chang RF, Moon WK, Lee CC. 2002. Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound. Ultrasound Med Biol 28(7):897-902.
Chang RF, Wu WJ, Moon WK, Chen DR. 2003. Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound Med Biol 29(5):679-686.
Sawaki A, Shimamoto K, Satake H, Ishigaki T, Koyama S, Obata Y, Ikeda M. 1999. Breast ultrasonography: diagnostic efficacy of a computer-aided diagnostic system using fuzzy inference. Radiat Med 17(1):41-45.
Rohling RN, Gee AH, Berman L. 1998. Automatic registration of 3D ultrasound images. Ultrasound Med Biol 24(6):841-854.
Suri JS, Liu K, Singh S, Laxminarayan SN, Zeng X, Reden L. 2002. Shape recovery algorithms using level sets in 2D/3D medical imagery: a state-of-the-art review. IEEE Trans Inf Technol Biomed 6(1):8-28.
Suri JS, Singh S, Reden L. 2002. Fusion of region and boundary/surface-based computer vision and pattern recognition techniques for 2D and 3D MR cerebral cortical segmentation (Part II): a state-of-the-art review. Pattern Anal Appl 5(1):77-98.
Suri JS, Wu D, Reden L, Gao J, Singh S, Laxminarayan S. 2001. Modeling segmentation via geometric deformable regularizers, PDE and level sets in still/motion imagery: a revisit. Int J Image Graphics 1(4):681-734.
Sethian JA. 1999. Level set methods and fast marching methods: evolving interfaces in compu- tational geometry, fluid mechanics, computer vision, and materials science, 2nd ed. Cambridge: Cambridge UP.
Sethian JA, Vemuri BC, Malladi BC. 1995. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Machine Intell 17(2):158-175.
Sussman M, Fatemi E. 1999. An efficient, interface-preserving level set redistancing algorithm and its application to interfacial incompressible fluid flow. SIAM J Sci Comput 20(4):1165-1191.
Moon WK, Chang RF, Chen CJ, Chen DR, Chen WL. 2005. Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression. Radiology 236(2):458-464.
Chang RF, Wu WJ, Moon WK, Chen DR. 2005. Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res Treat 89(2):179-185.
Suri JS. 2001. Fast MR brain segmentation using regional level sets. Int J Eng Med Biol 20(4):84-95.
Suri JS, Singh S, Reden L. 2002. Computer vision and pattern recognition techniques for 2D and 3D MR cerebral cortical segmentation (part I): a state-of-the-art review. IEEE Trans Pattern Anal Machine Intell 5(1):46-76.
Chang RF, Chen DR, Huang YL. 2006. Computer-aided diagnosis for 2d/3d breast ultrasound. In Recent advances in breast imaging, mammography, and computer-aided diagnosis of breast cancer, pp. 112-196. Ed JS Suri, RM Rangayyan. Bellingham, WA: SPIE.
Stavros AT, Thickman D, Rapp CL, Dennis MA, Parker SH, Sisney GA. 1995. Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology 196 (1):123-134.
Cespedes I, Ophir J, Ponnekanti H, Maklad N. 1993. Elastography: elasticity imaging using ultrasound with application to muscle and breast in vivo. Ultrason Imaging 15(2):73-88.
Ophir J, Cespedes I, Ponnekanti H, Yazdi Y, Li X. 1991. Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging 13(2):111-134.
Horsch K, Giger ML, Vyborny CJ, Venta LA. 2004. Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography. Acad Radiol 11(3):272-280.
Chen CM, Chou YH, Han KC, Hung GS, Tiu CM, Chiou HJ, Chiou SY. 2003. Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. Radiology 226(2):504-514.
Osher S, Sethian J. 1988. Fronts propagating with curvature-dependent speed: Algorithms based on the Hamilton-Jacobi formulation. J Comput Phys 79(1):12-49.
Sethian JA. 1990. Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws. J Differ Geom 31:131-161.
Suri JS, Laxminarayan SN. 2001. PDE and level sets: algorithmic approaches to static and motion imagery. New York: Springer.
Mulder W, Osher S, Sethian J. 1992. Computing interface motion in compressible gas dynamics. J Comput Phys 100(2):209-228.
Grayson M. 1987. The heat equation shrinks embedded plane curves to round points. J Differ Geom 26:285-314.
Gerig G, Kubler O, Kikinis R, Jolesz FA. 1992. Nonlinear anisotropic filtering of MRI data. IEEE Trans Med Imaging 11(2):221-232.
Perona P, Malik J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Machine Intell 12(7):629-639.
Czerwinski RN, Jones DL, O’Brien Jr WD. 1994. Edge detection in ultrasound speckle noise. In Proceedings of the IEEE International Conference on Image Processing, pp. 304-308. Washington, DC: IEEE Computer Society.
Czerwinski RN, Jones DL, O’Brien Jr WD. 1998. Line and boundary detection in speckle images. IEEE Trans Image Process 7(12):1700-1714.
Czerwinski RN, Jones DL, O’Brien Jr WD. 1999. Detection of lines and boundaries in speckle images: application to medical ultrasound. IEEE Trans Med Imaging 18(2):126-136.
Leymarie FL. 1993. Tracking deformable objects in the plane using an active contour model. IEEE Trans Pattern Anal Machine Intell 15(6):617-634.
Richardson IEG. 2003. H.264 and MPEG-4 video compression: video coding for next- generation multimedia. Chichester, UK: John Wiley & Sons.
Russ JC. 2002. The image processing handbook, 4th ed. Boca Raton, FL: CRC Press.
Vapnik VN. 1999. The nature of statistical learning theory, 2nd ed. New York: Springer.
Pontil M, Verri A. 1998. Support vector machines for 3D object recognition. IEEE Trans Pattern Anal Machine Intell 20(6):637-646.
Chapelle O, Haffner P, Vapnik VN. 1999. Support vector machines for histogram-based image classification. IEEE Trans Neural Networks 10(5):1055-1064.
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Suri, J.S., Chang, RF., Chen, WL., Tsai, CL., Chen, CJ. (2007). Breast Strain Imaging: A Cad Framework. In: Deformable Models. Topics in Biomedical Engineering. International Book Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68343-0_8
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