Abstract
Counting of white blood cells (WBCs) and detecting the morphological abnormality of these cells allow for diagnosis some blood diseases such as leukemia. This can be accomplished by automatic quantification analysis of microscope images of blood smear. This paper is oriented towards presenting a novel framework that consists of two sub-systems as indicators for detection Acute Lymphoblastic Leukemia (ALL). The first sub-system aims at counting WBCs by adapting a deep learning based approach to separate agglomerates of WBCs. After separation of WBCs, we propose the second sub-system to detect and count abnormal WBCs (lymphoblasts) required to diagnose ALL. The performance of the proposed framework is evaluated using ALL-IDB dataset. The first presented sub-system is able to count WBCs with an accuracy up to 97.38%. Furthermore, an approach using ensemble classifiers based on handcrafted features is able to detect and count the lymphoblasts with an average accuracy of 98.67%.
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References
Inaba, H., Greaves, M., Mullighan, C.: Acute lymphoblastic leukaemia. Lancet 381(9881), 1943–1955 (2013)
Briggs, C., Longair, I., Slavik, M., Thwaite, K., Mills, R., Thavaraja, V., Foster, A., Romannin, D., Machin, S.: Can automated blood film analysis replace the manual differential? An evaluation of the CellaVision DM96 automated image analysis system. Lab. Hematol. 31(1), 48–60 (2009)
Le, D., Bui, A., Yu, Z., Bui, F.: An automated framework for counting lymphocytes from microscopic images. In. Computing and Communication (IEMCON), pp. 1–6. Vancouver (2015)
Putzu, L., Caocci, G., Di Ruberto, C.: Leucocyte classification for leukaemia detection using image processing techniques. Artif. Intell. Med. 62(3), 179–191 (2014)
Bhavnani, L., Jaliya, U., Joshi, M.: Segmentation and counting of WBCs and RBCs from microscopic blood sample images. Image, Graph. Signal Process. 8(11), 32 (2016)
Basima, C.T., Panicker, J.: Enhanced leucocyte classification for leukaemia detection. In: Information Science (ICIS), Kochi, pp. 65–71 (2016)
Alomari, Y., Abdullah, S., Azma, R., Omar, K.: Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm. In: Computational and Mathematical Methods in Medicine 2014 (2014)
Di Ruberto, C., Loddo, A., Putzu, L.: A leukocytes count system from blood smear images. Mach. Vis. Appl. 27(8), 1151–1160 (2016)
Abd Halim, N., Mashor, M., Hassan, R.: Automatic blasts counting for acute leukemia based on blood samples. Res. Rev. Comput. Sci. (IJRRCS) 2(4), 971 (2011)
ALL-IDB Homepage. https://homes.di.unimi.it/scotti/all/. Accessed 10 May 2017
Duggal, R., Gupta, A., Gupta, R., Wadhwa, M., and Ahuja, C.: Overlapping cell nuclei segmentation in microscopic images using deep belief networks. In: Computer Vision Graphics and Image Processing, Guwahati, p. 82 (2016)
Cseke, I.: A fast segmentation scheme for white blood cell images. In: Pattern Recognition, The Hague, pp. 530–533 (1992)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Mohapatra, S., Patra, D.: Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images. In: Systems in Medicine and Biology (ICSMB), Kharagpur, vol. 62, no. 3, pp. 49–54 (2010)
Liu, L., Lao, S., Fieguth, P., Guo, Y., Wang, X., Pietikinen, M.: Median robust extended local binary pattern for texture classification. IEEE Trans. Image Process. 25(3), 1368–1381 (2016)
Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Busch, A., Boles, W.: Texture classification using multiple wavelet analysis. In: Digital Image Computing Techniques and Applications, pp. 341–345 (2002)
Smetana, K., Jirásková, I., Starỳ, J.: The number of nucleoli and main nucleolar types in lymphoblasts of children suffering from acute lymphoid leukemia. Hematol. J. 4(3), 231–236 (1999)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)
Bishop, C.: Mach. Learn. Pattern Recogn. Springer, Heidelberg (2006)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
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This research was supported by the Natural Sciences and Engineering Research Council of Canada.
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Ben-Suliman, K., Krzyżak, A. (2018). Computerized Counting-Based System for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Images. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_17
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DOI: https://doi.org/10.1007/978-3-030-01421-6_17
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