References
- [1] Lehman CD, Schnall MD. Imaging in breast cancer: magnetic resonance imaging. Breast Cancer Res. 2005; 7(5):215-219.
- [2] Herdman R, Norton L, editors. Saving women's lives: strategies for improving breast cancer detection and diagnosis. Washington (DC): National Academies Press; 2005.
- [3] Kerlikowske K, Carney PA, Geller B, et al. Performance of screening mammography among women with and without a first-degree relative with breast cancer. Ann Intern Med. 2000;133(11):855-863.
- [4] 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. 2002;225(1):165-175.
- [5] Bird RE, Wallace TW, Yankaskas BC. Analysis of cancers missed at screening mammography. Radiology. 1992;184(3):613-617.
- [6] Kopans DB. The positive predictive value of mammography. AJR Am J Roentgenol. 1992;158(3):521-526.
- [7] Reiser I, Nishikawa RM, Giger ML, et al. Computerized detection of mass lesions in digital breast tomosynthesis images using twoand three dimensional radial gradient index segmentation. Technol Cancer Res Treat. 2004;3(5):437-441.
- [8] Pavic D, Koomen M, Kuzmiak C, et al. Ultrasound in the management of breast disease. Curr Womens Health Rep. 2003;3(2):156-164.
- [9] Weir L, Worsley D, Bernstein V. The value of FDG positron emission tomography in the management of patients with breast cancer. Breast J. 2005;11(3):204-209.
- [10] Eubank WB, Mankoff DA. Evolving role of positron emission tomography in breast cancer imaging. Semin Nucl Med. 2005;35(2):84-99.
- [11] Sardanelli F, Giuseppetti GM, Panizza P, et al. Sensitivity of MRI versus mammography for detecting foci of multifocal, multicentric breast cancer in fatty and dense breasts using the whole-breast pathologic examination as a gold standard. AJR Am J Roentgenol. 2004;183(4);1149-1157.
- [12] Liberman L, editor. Breast MRI: diagnosis and intervention. Springer; 2005.
- [13] Heiberg EV, Perman WH, Herrmann VM, et al. Dynamic sequential 3D gadolinium-enhanced MRI of the whole breast. Magn Reson Imaging. 1996;14(4):337-348.
- [14] Cardillo FA, Francesco M. Image analysis methods in MRI examinations of the breast. Università di Pisa: Technical Report TR-09-16; 2009.
- [15] Coto E, Grimm S. Bruckner S, et.al. MammoExplorer: An advanced CAD application for breast DCE-MRI. In: Proceedings of Vision, Modelling, and Visualization 2005. 2005;91-98.
- [16] Sathya DJ, Geetha K. Development of intelligent system based on artificial swarm bee colony clustering algorithm for efficient mass extraction from breast DCE-MR Images. Int J Recent Trends Engineering and Technology. 2011;6(1):82-88.
- [17] Sathya DJ, Geetha K. Development of CAD system based on enhanced clustering based segmentation algorithm for detection of masses in breast DCE-MRI. Int J Comput Science Issues. 2011;8(5):378-387.
- [18] Liang X, Ramamohanara K, Frazer H, et al. Lesion Segmentation in Dynamic Contrast Enhanced MRI of Breast. 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA). Fremantle, WA. 2012;1-8.
- [19] Meinel LA, Buelow T, Huo D, et al. Robust segmentation of mass‐lesions in contrast‐enhanced dynamic breast MR images. Journal of magnetic resonance imaging. 2010;32(1):110-119.
- [20] Pang Y, Li L, Hu W, Peng Y, et al. Computerized segmentation and characterization of breast lesions in dynamic contrast-enhanced MR images using fuzzy c-means clustering and snake algorithm. Computational and Mathematical Methods in Medicine. 2012. Article ID 634907.
- [21] Baltzer PA, Dietzel M, Kaiser WA. Nonmass lesions in magnetic resonance imaging of the breast: additional T2-weighted images improve diagnostic accuracy. J Comput Assist Tomogr. 2011;35(3):361-366.
- [22] Ertaş G, Gülçür HÖ, Osman O, et al. Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching. Computers in biology and medicine. 2008;38(1):116-126.
- [23] Tan Y, Liu L, Liu Q, \et al. Automatic breast DCE-MRI segmentation using compound morphological operations. 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). Shanghai. 2011;147-150.
- [24] Li XL, Shao ZJ, Qian JX. An optimizing method based on autonomous animats: fish-swarm algorithm. System Engineering Theory and Practice. 2002;22(11):32-38.
- [25] Rocha A, Fernandes E, Martins T. Novel fish swarm heuristics for bound constrained global optimization problems. In: Murgante B, Gervasi O, Iglesias A, et al. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6784. Springer, Berlin, Heidelberg.
- [26] He S, Belacel N, Hamam H, et al. Fuzzy clustering with improved artificial fish swarm algorithm. 2009 International Joint Conference on Computational Sciences and Optimization. Sanya, Hainan. 2009;317-321.
- [27] Xiao L. A clustering algorithm based on artificial fish school. 2010 2nd International Conference on Computer Engineering and Technology. Chengdu. 2010;V7-766-V7-769.
- [28] Zhang M, Shao C, Li M, Sun J. Mining classification rule with artificial fish swarm. 2006 6th World Congress on Intelligent Control and Automation. Dalian. 2006;5877-5881.
- [29] Cui G, Cao X, Zhou J, et al. The optimization of DNA encoding sequences based on improved AFS algorithms. 2007 IEEE International Conference on Automation and Logistics. Jinan. 2007;1141-1144.
- [30] Azad MA, Rocha AM, Fernandes EM. Improved binary artificial fish swarm algorithm for the 0-1 multidimensional knapsack problems. Swarm and Evolutionary Computation. 2014;14:66-75.
- [31] Janaki Sathya D, Geetha K. Comparative study of different edge enhancement filters in spatial domain for magnetic resonance images. AMSE Journal. 2011;54(1):30-43.
- [32] Weszka JS. A survey of threshold selection techniques. Computer Graphics and Image Processing. 1978;7(2):259-265.
- [33] Swets JA. ROC analysis applied to the evaluation of medical imaging techniques. Invest Radiol. 1979;14(2):109-121.
- [34] Janaki Sathya D, Geetha K. A comparison of certain soft computing techniques for segmentation of ROI from breast DCE-MR Images. Karpagam Journal of Computer Science. 2013;7(3):116-128.
- [35] Sathya DJ, Geetha K. Quantitative comparison of artificial honey bee colony clustering and enhanced SOM based K-means clustering algorithms for extraction of ROI from breast DCE-MR images. International Journal on Recent Trends in Engineering and Technology. 2013;8(1):51-56.