Abstract
The counting and classification of blood cells allows the evaluation and diagnosis of a vast number of diseases, such as the ALL - Acute Lymphocytic Leukemia, detected through the analysis of white blood cells (WBCs). Nowadays the morphological analysis of blood cells is performed manually by skilled operators, involving numerous drawbacks, such as slowness of the analysis and a non-standard accuracy, dependent on the operator skills. In literature there are only few examples of automated systems able to process a whole image in order to analyze and classify all the WBCs included. This paper presents a complete and fully automatic method for WBCs identification from microscopic images and an evaluation of different classification model to determine the presence of leukemia. Experimental results show that the proposed method is able to identify the cells carrying leukemia and consequently to determine whether a patient is suffering from this disease.
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Biondi, A., Cimino, G., Pieters, R., Pui, C.H.: Biological and Therapeutic Aspects of Infant Leukemia. Blood 96(1), 24–33 (2000)
Cheewatanon, J., Leauhatong, T., Airpaiboon, S., Sangwarasilp, M.: A New White Blood Cell Segmentation Using Mean Shift Filter and Region Growing Algorithm. International Journal of Applied Biomedical Engineering 4, 30–35 (2011)
Cseke, I.: A Fast Segmentation Scheme for White Blood Cell Images. In: Proceedings of the IAPR International Conference on Image, Speech and Signal Analysis, vol. 3, pp. 530–533 (1992)
Donida Labati, R., Piuri, V., Scotti, F.: ALL-IDB: the Acute Lymphoblastic Leukemia Image DataBase for Image Processing. In: Proceedings of the ICIP International Conference on Image Processing, pp. 2045–2048 (2011)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pearson Prentice Hall Pearson Education, Inc., New Jersey (2004)
Kovalev, V.A., Grigoriev, A.Y., Ahn, H.: Robust Recognition of White Blood Cell Images. In: Proceedings of the 13th International Conference on Pattern Recognition, pp. 371–375 (1996)
Lindblad, J.: Development of algorithms for digital image cytometry. Uppsala University, Faculty of Science and Technology (2002)
Madhloom, H.T., Kareem, S.A., Ariffin, H., Zaidan, A.A., Alanazi, H.O., Zaidan, B.B.: An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automated Threshold. Journal of Applied Sciences 10(11), 959–966 (2010)
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979)
Scotti, F.: Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference, pp. 43–48 (April 2006)
Zack, G., Rogers, W., Latt, S.: Automatic Measurement of Sister Chromatid Exchange Frequency. Journal of Histochemistry and Cytochemistry 25, 741–753 (1977)
Putzu, L., Di Ruberto, C.: White Blood Cells Identification and Counting from Microscopic Blood Images. In: Proceedings of the WASET International Conference on Bioinformatics, Computational Biology and Biomedical Engineering, vol. 73, pp. 268–275 (January 2013)
Putzu, L., Di Ruberto, C.: White Blood Cells Identification and Classification from Leukemic Blood Image. In: Proceedings of the IWBBIO International Work-Conference on Bioinformatics and Biomedical Engineering, pp. 99–106 (March 2013)
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Putzu, L., Di Ruberto, C. (2013). Investigation of Different Classification Models to Determine the Presence of Leukemia in Peripheral Blood Image. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_62
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DOI: https://doi.org/10.1007/978-3-642-41181-6_62
Publisher Name: Springer, Berlin, Heidelberg
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