Abstract
The application of the convolution neural network for detection of the micro nucleuses in the human lymphocyte images acquired by the image flow cytometer is considered in this paper. The existing method of detection, called IMAQ Match Pattern, is described and its limitations concerning zoom factors are analyzed. The training algorithm of the convolution neural network and the detection procedure were described. The performance of both detection methods, convolution neural network and IMAQ Match Pattern, were researched. Our results show that the convolution neural network overcomes the IMAQ Match Pattern in terms of improvement of detection rate and decreasing the numbers of false alarms.
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References
Coskun, M., Top, A., Orta, T.: Biological Dosimetry Following X-ray Irradiation. Turkish Journal of Medical Science 30, 563–569 (2000)
Cram, L.S., Martin, J.C., Steinkamp, J.A., Yoshida, T.M., Buican, T.N., Marosiorone, B.L., Jett, J.H., Salzman, G., Sklar, L.: New Flow Cytometric Capabilities at the National Flow Cytometry Resource. Proc. of IEEE 80(6), 912–917 (1992)
Liu, Y., Fisher, A.C.: Human Erythrocyte Sizing and Deformability Study by Laser Flow Cytometer. In: Proc. of Ann. Int. Conf. of the IEEE Eng. in Medicine and Biology Society, vol. 1, pp. 324–325 (1992)
Maguire, D., King, G.B., Kelley, S., Durack, G., Robinson, J.P.: Computer-Assisted Diagnosis of Hematological Malignancies Using a Pattern Representation of Flow Cytometry Data. In: Proc. of 12th South. Biom. Eng. Conf., vol. 1, pp. 153–155 (1993)
Abate, G.F., Bavaro, F., Castello, G., Daponte, P., Grimaldi, D., Guglielmelli, G., Martinelli, F.U., Mauro, U., Moisa, S., Napolitano, M., Rapuano, S., Scerbo, P.: Tomography System to Acquire 3D Images of Cells in Laminar Flow: Hardware Architecture. In: Proc. Intern. Workshop on Medical Measurement and Applications MeMea 2006, Italy, pp. 68–73 (2006)
Grimaldi, D., Lamonaca, F.: Reduction of Doubtful Detection of Micro-nucleus in Human Lymphocyte. Int. J. Advan. Media and Comm. 3(1/2), 80–94 (2009)
Balestrieri, E., Grimaldi, D., Lamonaca, F., Rapuano, S.: Image Flow Cytometer. In: Murkopadhyay, S.C., Lay, E.A. (eds.) Adv. in Biomed. Sens., Meas., Instrum. and Syst. LNEE, vol. 55, pp. 210–239 (2010)
Relf, C.G.: Image Acquisition and Processing with LabVIEW, pp. 164–168. CRC Press, Boca Raton (2004)
Carnì, D.L., Grimaldi, D., Lamonaca, F.: Image Pre-processing for Micro Nucleuses Detection in Lymphocyte. Intern. Sci. J. of Computing 4(3), 63–69 (2005)
Carnì, D.L., Grimaldi, D., Lamonaca, F.: Pre-Processing Correction for Micro Nucleus Image Detection Affected by Contemporaneous Alterations. IEEE Transaction on I&M 56(4), 1202–1211 (2007)
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall, Englewood Cliffs (2008)
Yang, M.: Recent Advances in Face Detection. In: IEEE ICPR 2004, Tutorial, Cambridge, UK, 93 (2004)
Rowley, H., Baluja, S., Kanade, T.: Neural Network-based Face Detection. IEEE Trans. on Patt. Anal. and Mach. Intellig. 20, 22–88 (1998)
Garcia, C., Delakis, M.: Convolution Face Finder: A Neural Architecture for Fast and Robust Face Detection. IEEE Trans. on Pat. Anal. and Mach. Intellig. 26(11), 1408–1423 (2004)
Romdhani, S., Torr, P., Schlkopf, B., Blake, A.: Computationally Efficient Face Detection. In: Proceedings of ICCV, vol. 1, pp. 695–700 (2001)
Heisele, B., Serre, T., Prentice, S., Poggio, T.: Hierarchical Classification and Feature Reduction for Fast Face Detection with Support Vector Machines. Pattern Recognition 36(9), 2007–2017 (2003)
Schneiderman, H., Kanade, T.: Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition. In: Proc. IEEE Conf. Comp. Vision and Patt. Recog. pp. 45–51 (1998)
Viola, P., Jones, M.: Robust Real-Time Face Detection. Int. J. of Comp. Vis. 57(2), 137–154 (2004)
Lienhart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: Proc. of IEEE Inter. Conf. on Image Proc. vol. 1, pp. 900–903 (2002)
Li, S., Zhang, Z.: FloatBoost Learning and Statistical Face Detection. IEEE Trans. on Patt. Analys. and Mach. Intellig. 26(9), 1112–1123 (2004)
LeCun, Y., Bottou, L., Bengio, Y.: Gradient-Based Learning Applied to Document Recognition. Intellig. Sign. Proc., 306–351, IEEE Press (2001)
Simard, P., Steinkraus, D., Platt, J.: Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis. In: 7th Intern. Conf. on Document Analys. and Recogn., vol. 2, p. 958 (2003)
Paliy, I.: Human Face Detection Methods Using a Combined Cascade of Classifiers, Inter. J. of Computing 7(1), 114–125 (2004) (in Ukrainian)
Wasserman, A.: Neural Computing: Theory and Practice, vol. 230. Van Nostrand Reinhold, New York (1989)
Golovko, V., Galushkin, A.: Neural Networks: Training, Models and Applications. Radiotechnika, Moscow, 256 (2001) (in Russian)
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Paliy, I., Lamonaca, F., Turchenko, V., Grimaldi, D., Sachenko, A. (2010). Micro Nucleus Detection in Human Lymphocytes Using Convolutional Neural Network. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_68
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DOI: https://doi.org/10.1007/978-3-642-15819-3_68
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