Share This Article:

A Finite Element Model for Recognizing Breast Cancer

Abstract Full-Text HTML Download Download as PDF (Size:895KB) PP. 296-306
DOI: 10.4236/jbise.2014.75032    3,629 Downloads   5,161 Views   Citations

ABSTRACT

Breast cancer recognition is an important issue in elastography diagnostic imaging. Breast tumor biopsy has been for many years the reference procedure to assess histological definition for breast diseases. But biopsy measurement is an invasive method besides it takes larger time. So, fast and improved methods are needed. Using elastography technology, a digital image correlation technique can be used to calculate the displacement of breast tissue after it has suffered a compression force. This displacement is related to tissue stiffness, and breast cancer can be classified into benign or malignant according to that displacement. The value of compression force affects the displacement of tissue, and then affects the results of the breast cancer recognition. Finite element method was being used to simulate a model for the breast cancer as a phantom to be used in measurements and study of breast cancer diagnosis. The breast cancer using this phantom can be recognized within a short time. The proposed work succeeded in recognizing breast tumor phantom by an average correct recognition ratio CRR of about 94.25% on a simulation environment. The strain ratio SR for benign and malignant models is also computed. The result of the simulated breast tumor model is compared with real data of 10 lesion cases (6 benign and 4 malignant). The coefficient of variation CV between the simulated SR and the SR using real data reaches to about 5% for benign lesions and 4.78% for malignant lesions. The results of CRR and CV in this proposed work assure that the proposed breast cancer model using finite element modeling is a robust technique for breast tumor simulation where the behavior of real data of breast cancer can be predicted.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Wahba, A. , Khalifa, N. , Seddik, A. and El-Adawy, M. (2014) A Finite Element Model for Recognizing Breast Cancer. Journal of Biomedical Science and Engineering, 7, 296-306. doi: 10.4236/jbise.2014.75032.

References

[1] National Cancer Institute (2014) General Information about Breast Cancer—Stages of Breast Cancer. At the National Institutes of Health.
http://www.cancer.gov/cancertopics/pdq/treatment/breast/Patient/page2
[2] NICE Clinical Guideline 80 (2009) Early and Locally Advanced Breast Cancer: Diagnosis and Treatment. Developed by the National Collaborating Centre for Cancer. National Institute for Health and Clinical Excellence, February.
[3] Khatib, O.M.N. and Modjtabai, A. (2006) Guidelines for the Early Detection and Screening of Breast Cancer. EMRO Technical Publications Series 30, World Health Organization.
[4] Treece, G., Lindop, J., Chen, L., Housden, J., Prager, R. and Gee, A. (2011) Real-Time Quasi-Static Ultrasound Elastography. Interface Focus, 1, 540-552.
http://rsfs.royalsocietypublishing.org/content/1/4/540.full.html#related-urls
[5] Sette, M.M., Goethals, P., D’hooge, J., Van Brussel, H. and Vander Sloten, J. (2011) Algorithms for Ultrasound Elastography: A Survey. Computer Methods in Biomechanics and Biomedical Engineering, 14, 283-292.
http://dx.doi.org/10.1080/10255841003766837
[6] Oelze, M.L., O’Brien, W.D. and Zachary, J.F. (2007) Quantitative Ultrasound Assessment of Breast Cancer Using a Multiparameter Approach. IEEE Ultrasonics Symposium, New York, 28-31 October, 981-984.
[7] Hirooka, Y., Aika, N., Ishisugi, T., Ohguri, M., Nagashima, C., Morishita, S., Kato, Y. and Fukuda, C. (2009) Recent Advances in Ultrasound Imaging of Breast Lesions. Yonago Acta Medica, 52, 115-120.
[8] Chang, R.-F., Shen, W.-C., Yang, M.-C., Moon, W.K., Takada, E., Ho, Y.-C., Nakajima, M. and Kobayashi, M. (2008) Computer-Aided Diagnosis of Breast Color Elastography. Proceedings of SPIE, Medical Imaging, Computer-Aided Diagnosis, 6915, Article ID: 69150I.
[9] Selvan, S., Kavitha, M., Shenbagadevi, S. and Suresh, S. (2010) Feature Extraction for Characterization of Breast Lesions in Ultrasound Echography and Elastography. Journal of Computer Science, 6, 67-74.
http://dx.doi.org/10.3844/jcssp.2010.67.74
[10] Kumm, T.R. and Szabunio, M.M. (2010) Elastography for the Characterization of Breast Lesions: Initial Clinical Experience. Cancer Control: Journal of the Moffitt Cancer Center, 17, 156.
[11] de Faria Castro Fleury, E., Fleury, J.C.V., Piato, S. and Roveda, D. (2009) New Elastographic Classification of Breast Lesions during and after Compression. Diagnostic and Interventional Radiology, 15, 96-103.
[12] Lutz, H. and Buscarini, E. (2011) Manual of Diagnostic Ultrasound. Vol. 1, 2nd Edition, World Health Organization.
[13] Wells, P.N.T. (1999) Ultrasound Imaging of the Human Body. Reports on Progress in Physics, 62, 671-722.
http://dx.doi.org/10.1088/0034-4885/62/5/201
[14] Balleyguier, C., Canale, S., Hassen, W.B., et al. (2012) Breast Elasticity: Principles, Technique, Results: An Update and Overview of Commercially Available Software. European Journal of Radiology, 82, 427-434.
http://dx.doi.org/10.1016/j.ejrad.2012.03.001
[15] Pellot-Barakat, C., Sridhar, M., Lindfors, K.K. and Insana, M.F. (2006) Ultrasonic Elasticity Imaging as a Tool for Breast Cancer Diagnosis and Research. Current Medical Imaging Reviews, 2, 157.
http://dx.doi.org/10.2174/157340506775541631
[16] Wahba, A.A., Khalifa, N.M.M., Seddik, A.F. and El-Adawy, M.I. (2013) Liver Fibrosis Recognition Using Multi-Compression Elastography Technique. Journal of Biomedical Science and Engineering, 6, 1034-1039.
[17] Krousko, T.A., Wheeler, T.M., Kallel, F., Garra, B.S. and Hall, T. (1998) Elastic Moduli of Breast and Prostate Tissues under Compression. Ultrasonic Imaging, 20, 260-274.
http://dx.doi.org/10.1177/016173469802000403
[18] Dang, J., Lasaygues, P., Zhang, D.C., et al. (2009) Development of Breast Anthropomorphic Phantoms for Combined PET-Ultrasound Elastography Imaging. IEEE International Medical Imaging Conference, 1-11.
[19] Kumar, K., Andrews, M.E., Jayashankar, V., Mishra, A.K. and Suresh, S. (2009) Improvement in Diagnosis of Breast Tumor Using Ultrasound Elastography and Echography: A Phantom Based Analysis. Biomedical Imaging and Intervention Journal, 5, Article ID: e30.
http://www.biij.org/2009/4/e30/
http://dx.doi.org/10.2349/biij.5.4.e30
[20] (2012) ABAQUS Installation and Licensing Guide.
http://www.3ds.com/products/simulia/overview/
[21] (2012) ABAQUS Tutorial. EN175: Advanced Mechanics of Solids. Division of Engineering Brown University.
http://www.brown.edu/Departments/Engineering/Courses/En175/
[22] (2011) Abaqus 6.11 Analysis User’s Manual. Vol. 5, Prescribed Conditions, Constraints & Interactions. Product of Dassault Systèmes Simulia Corp, Providence.
[23] Pinidiyaarachchi, A. (2009) Digital Image Analysis of Cells: Applications Are in 2D, 3D and Time. Uppsala University, Uppsala, 596.
[24] Deserno, T.M. (2011) Fundamentals of Biomedical Image Processing. In: Biomedical Engineering, Springer-Verlag, Berlin, 1-51.
http://dx.doi.org/10.1007/978-3-642-15816-2_1
[25] Su, C. and Anand, L. (2003) A New Digital Image Correlation Algorithm for Whole Field Displacement Measurement. Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge, MA 02139.
[26] Eberl, C., Thompson, R. and Gianola, D. (2013) Digital Image Correlation and Tracking with Matlab.
http://www.mathworks.com/matlabcentral/fileexchange/12413-digital-image-correlation-and-tracking
[27] Zhang, D.S. and Arola, D.D. (2004) Applications of Digital Image Correlation to Biological Tissues. Journal of Biomedical Optics, 9, 691-699.
http://dx.doi.org/10.1117/1.1753270
[28] Yoneyama, S. and Murayama, G. (2007) Digital Image Correlation. Experimental Mechanics, Encyclopedia of Life Support Systems (EOLSS).
[29] Tong, W. (2005) An Evaluation of Digital Image Correlation Criteria for Strain Mapping Applications. Strain, 41, 167-175.
http://dx.doi.org/10.1111/j.1475-1305.2005.00227.x
[30] Cocco, A. and Masin, S.C. (2010) The Law of Elasticity. University of Padua. Psicologica, 31, 647-657.
[31] Chen, E.J., Novakofski, J., Jenkins, W.K. and O’Brien, W.D. (1996) Young’s Modulus Measurements of Soft Tissues with Application to Elasticity Imaging. IEEE Transducers on Ultrasonics, Ferroelectrics, and Frequency Control, 43, 191-194.
[32] Sette, M.M., Camino, J.F., D’hooge, J., Van Brussel, H. and Vander Sloten, J. (2007) Comparing Optimization Algorithms for Young’s Modulus Reconstruction in Ultrasound Elastography. Mechanical Department Katholieke Universiteit, Leuven, BELGIUM, IEEE Ultrasonics Symposium, New York, 28-31 October 2007, 2028-2031.
[33] Yeh, W.C., Jeng, Y.M., et al. (2001) Young’s Modulus Measurements of Human Liver and Correlation with Pathological Findings. IEEE Ultrasonics Symposium, Atlanta, 7-10 October 2001, 1233.
[34] Benson, J. and Fan, L.X. (2012) Tissue Strain Analytics—A Complete Ultrasound Solution for Elastography. Siemens Medical Solutions USA, Inc.
[35] Carlsen, J.F., Ewertsen, C., Lönn, L. and Nielsen, M.B. (2013) Strain Elastography Ultrasound: An Overview with Emphasis on Breast Cancer Diagnosis. Diagnostics, 3, 117-125.
www.mdpi.com/journal/diagnostics/
http://dx.doi.org/10.3390/diagnostics
[36] Wahba, A.A., Khalifa, N.M.M., Seddik, A.F. and El-Adawy, M.I. (2013) Improvement of Breast Cancer Diagnosis Using Acoustic Impedance Matching. Jokull Journal, 63, 205-213.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.