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A Novel Approach to Detect Microcalcification in Mammogram Image using Evolutionary Algorithm

R. Sivakumar, Marcus Karnan

Abstract


Breast cancer is one of the major causes for the increase
in mortality among women, especially in developed countries. Brea cancer is the second most common cancer in women. The presence of microcalcifications in breast tissue is one of the most important signs considered by radiologist for an early diagnosis of breast cancer, which is one of the most common forms of cancer among women. In this
paper, detection of microcalcification is performed in two steps: preprocessing& enhancement, segmentation. First, the thresholding algorithm is applied for the breast boundary identification and a new proposed modified tracking algorithm is introduced for pectoral muscle determination in Mammograms. Second, the Genetic Algorithm (GA) and Artificial Bee Colony(ABC) is proposed to automatically detect the breast border and nipple position to identify the suspicious regions on digital mammograms based on asymmetries between left and right breast image. The basic idea of the asymmetry approach is corresponding left and right images are subtracted to extract the suspicious region. The algorithms are tested on 161 patient’s digitized mammograms from MIAS database. In general theproposed GA, ABC and Bilateral algorithms are quite competitive with the other algorithms.


Keywords


Genetic Algorithm (GA), Artificial Bee Colony (ABC), Microcalcifications

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References


A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing, Springer, 2003.

R.C. Eberhart, Y. Shi, J. Kennedy, Swarm Intelligence, Morgan Kaufmann, 2001.

Cheng, H.D., Cai, X., Chen, X.W., Hu, L., and Lou, X.: “Computer Aided Detection and Classification of Microcalcifications in

Mammograms: A Survey,” Pattern Recognition, vol. 36, pp: 2967–2991,2003

Ferrari, R.J., Rangayyan, R.M., Desautels, J.E.L., Borges, R.A., and Frere, A.F.: “Analysis of Asymmetry in Mammograms via Directional Filtering With Gabor Wavelets,” IEEE Transactions on Medical Imaging,vol. 20, no. 9, pp: 953–964, 2001.

Goldberg, D.E.: “Genetic Algorithms in Search,” Optimization and Learning, NY, Addison Wesley, 1989.

Dervis Karaboga, Bahriye Akay,” A comparative study of Artificial BeeColony algorithm” Journal of Applied Mathematics and Computation,Elsevier Inc, 214 (2009) 108–132

D. Karaboga, B. Basturk, A. powerful, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm,Journal of Global Optimization 39 (3) (2007) 459–471.

D. Karaboga, B. Basturk, On the performance of artificial bee colony(abc) algorithm, Applied Soft Computing 8 (1) (2008) 687–697.

D. Karaboga, B. Basturk, in: Advances in Soft Computing: Foundation of Fuzzy Logic and Soft Computing, LNCS, vol. 4529/2007 Springer-Verlag, 2007, pp. 789–798.

D. Karaboga, B. Basturk Akay, C. Ozturk, in: Modeling Decisions for Artificial Intelligence, LNCS, vol. 4617/2007, Springer-Verlag, 2007, pp.318–329.

D. Karaboga, B. Basturk Akay, An artificial bee colony (abc) algorithm on training artificial neural networks, in: 15th IEEE Signal Processing and Communications Applications, SIU 2007, Eskisehir, Turkiye, June,pp. 1–4.

N. Karaboga. A new design method based on artificial bee colony algorithm for digital iir filters, Journal of The Franklin Institute 346 (4)(2009) 328–348.

Lau, T.K., and Bischof, W.F.: “Automated detection of breast tumorsusing the asymmetry approach,” Computers and Biomedical Research,vol. 24, pp: 273-295, 1991.

Mendez, A.J., Tahocesb, P.G., Lado, M. J., Souto, M., Correa, J.L., andVidal, J.J.: “Automatic Detection of Breast Border and Nipple in DigitalMammograms,” Computer Methods and Programs in Biomedicine, vol.49, pp: 253–262, 1996.

Muttarak, M., Peh, G., and Chaiwun, B.: “Malignant germ cell tumors ofundescended testes: imaging features with pathological correlation,” Clinical Radiology, vol. 59, pp: 198–204, 2004.

Sallam, M.Y., and Bowyer, K.W.: “Registration and difference analysis of corresponding mammogram images,” Medical Image Analysis, vol. 3, no. 2, pp: 103-118, 1999.

Thangavel, K., Karnan, M., Siva Kumar, R., and Kaja Mohideen, A.:“Automatic Detection of Microcalcification in Mammograms-A Review,” International Journal on Graphics Vision and Image Processing,vol. 5, no. 5, pp: 31-61, 2005.

Thangavel, K., Karnan, M., Siva Kumar, R., and Kaja Mohideen, A.:“Segmentation and Classification of Microcalcification in Mammograms Using the Ant Colony System,” International Journal on Artificial Intelligence and Machine Learning, vol. 5, no.3, pp: 29-40, 2005.

Bick, U., Giger, M.L., Schmidt. R.A., Nishikawa, R.M., Wolverton,D.E., and Doi, K.: “Automated segmentation of digitizedmammograms,” Academic Radiology, vol. 2, pp: 1-9, 1995.

Clarke, L.P., Kallergi, M., Qian, W., Li, H.D., Clark, R.A., and Silbiger, M.L.: “Tree-structured non-linear filter and wavelet transform for microcalcification segmentation in digital mammography,” Cancer Lett., vol. 7, pp: 173–181, 1994.

Kopans, D.B.: “Breast Imaging,” Lippincott Company, Philadelphia,1989.

Qian, W., Clarke, L.P., Kallergi, M., Li, H., Velthuizen, R., Clark, R.A., and Silbiger, M.L.: “Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography,” SPIE Biomed. Image Processing and Biomed. Visual, vol. 1905, pp:509–520, 1993.

Tabar, L., and Dean, P.B.: “Teaching Atlas of Mammography,” New York, Thieme Inc., second ed., 1985.

Wirth, M.A., and Stapinski, A.: “Segmentation of the Breast Region in Mammograms Using Active Contours,” Dept. of Computing and Information Science, University of Guelph. USA, 1998.


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