Abstract
This paper proposes an automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. It is a challenging issue to segment leukocytes under uneven imaging conditions since features of microscopic leukocyte images change in different laboratories. Therefore, this paper introduces an automatic robust method to segment leukocyte from blood microscopic images. The proposed robust segmentation technique was designed based on the fact that if background and erythrocytes could be removed from the blood microscopic image, the remainder area will indicate leukocyte candidate regions. A new set of features based on hematologist visual criteria for the recognition of malignant leukocytes in blood samples comprising shape, color, and LBP-based texture features are extracted. Two new ensemble classifiers are proposed for healthy and malignant leukocytes classification which each of them is highly effective in different levels of analysis. Experimental results demonstrate that the proposed approach effectively segments leukocytes from various types of blood microscopic images. The proposed method performs better than other available methods in terms of robustness and accuracy. The final accuracy rate achieved by the proposed method is 98.10% in cell level. To the best of our knowledge, the image level test for acute lymphoblastic leukemia (ALL) recognition was performed on the proposed system for the first time that achieves the best accuracy rate of 89.81%.
Similar content being viewed by others
References
Hall J: Guyton and hall textbook of medical physiology. Elsevier Health Sciences, 2010
Inaba H, Greaves M, Mullighan C: Acute lymphoblastic leukaemia. The Lancet 381(9881):1943–1955, 2013
G. Voigt and S. Swist, Hematology techniques and concepts for veterinary technicians, John Wiley & Sons., 2011.
Münzenmayer C, Schlarb T, Steckhan D, Haßlmeyer E, Bergen T, Aschenbrenner S, Wittenberg T, Weigand C, Zerfaß T: HemaCAM--A computer assisted microscopy system for hematology. In: Microelectronic systems. Berlin Heidelberg: Springer, 2011, pp. 233–242
R. D. Labati, V. Piuri and F. Scotti, "All-IDB: The acute lymphoblastic leukemia image dataset for image processing," in Proc. IEEE ICIP, Brussels, Belgium, 2011.
M. Habibzadeh, A. Krzyzak, T. Fevens and A. Sadr, "Counting of RBCs and WBCs in noisy normal blood smear microscopic images," in Proc. SPIE7963, 2011.
Mohammed EA, Mohamed MM, Far BH, Naugler C: Peripheral blood smear image analysis: a comprehensive review. Journal of pathology informatics 5, 2014
Wermser D, Haussmann G, Liedtke C-E: Segmentation of blood smears by hierarchical thresholding. Computer Vision, Graphics, and Image Processing 25(2):151–168, 1984
Osuna V, Cuevas E, Sossa H: Segmentation of blood cell images using evolutionary methods. AISC, Springer 175:299–311, 2013
Ong S-H, Lim J-H, Foong K, Liu J, Racoceanu D, Chong A, Tan K: Automatic area classification in peripheral blood smears. Biomedical Engineering, IEEE Transactions on 57(8):1982–1990, 2010
Ghosh M, Chakraborty C, Konar A, Ray AK: Development of hedge operator based fuzzy divergence measure and its application in segmentation of chronic myelogenous leukocytes from microscopic image of peripheral blood smear. Micron 57:41–55, 2014
Ghosh M, Das D, Chakraborty C, Ray AK: Automated leukocyte recognition using fuzzy divergence. Micron 41(7):840–846, 2010
M. Hamghalam, M. Motameni and A. E. Kelishomi, Leukocyte segmentation in giemsa-stained image of peripheral blood smears based on active contour, in Proc. Signal Progcessing Systems, singapore, 2009.
Jati A, Singh G, Mukherjee R, Ghosh M, Konar A, Chakraborty C, Nagar AK: Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding. Micron 58:55–65, 2014
K. Jiang, Q. -M. Liao and S. -Y. Dai, A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering, in Proc. Machine Learning and Cybernetics, 2003.
Ko BC, Gim J-W, Nam J-Y: Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron 42(7):695–705, 2011
Li K, Lu Z, Liu W, Yin J: Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake. Pattern Recognition 45(4):1255–1264, 2012
Pan C, Park DS, Yoon S, Yang JC: Leukocyte image segmentation using simulated visual attention. Expert Systems with Applications 39(8):7479–7494, 2012
Saraswat M, Arya K: Automated microscopic image analysis for leukocytes identification: a survey. Micron 65:20–33, 2014
F. Zamani and R. Safabakhsh, An unsupervised GVF snake approach for white blood cell segmentation based on nucleus, in Proc. Signal Processing, Beijing, 2006.
Rezatofighi SH, Soltanian-zadeh H: Automatic recognition of five types of white blood cells in peripheral blood. Computerized Medical Imaging and Graphics 35(4):333–343, 2011
Kass M, Witkin A, Terzopoulos D: Snakes: active contour models. International Journal of Computer Vision 1(4):321–331, 1988
Huang D-C, Hung K-D, Chan Y-K: A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images. Journal of Systems and Software 85(9):2104–2118, 2012
Neoh S, Srisukkham W, Zhang L, Todryk S, Greystoke B, Lim C, Hossain M, Aslam N: An intelligent decision support system for leukaemia diagnosis using microscopic blood images. Scientific reports 5, 2015
Escalante H, Montes-y-Gómez M, González J, Gómez-Gil P, Altamirano L, Reyes C, Reta C, Rosales A: Acute leukemia classification by ensemble particle swarm model selection. Artificial intelligence in medicine 55(3):163–175, 2012
Fatichah C, Tangel M, Yan F, Betancourt JP, Widyanto MR, Dong F, Hirota K: Fuzzy feature representation for white blood cell differential counting in acute leukemia diagnosis. International Journal of Control, Automation and Systems 13(3):1–11, 2015
Putzu L, Caocci G, Di Ruberto C: Leucocyte classification for leukaemia detection using image processing techniques. Artificial Intelligence in Medicine 62(3):179–191, 2014
S. Mohapatra, D. Patra and S. Satpathy, Automated leukemia detection in blood microscopic images using statistical texture analysis, in Proc. Communication, Computing & Security, 2011.
Agaian S, Madhukar M, Chronopoulos AT: Automated screening system for acute myelogenousl eukemia detection in blood microscopic images. IEEE Systems Journal 8(3):995–1004, 2014
Meyer F: Topographic distance and watershed lines. Signal processing 38(1):113–125, 1994
Tejinder S: Atlas and text of hematology. New Delhi: Avichal Pub-lishing Company, 2010
Rezaee K, Haddadnia J, Tashk A: Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization. Applied Soft Computing 52:937–951, 2017
Tashk A, Helfroush MS, Danyali H, Akbarzadeh-Jahromi M: Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features. Applied Mathematical Modelling 39(20):6165–6182, 2015
A.Tashk, MS. Helfroush, H. Danyali: A computer-aided system for automatic mitosis detection from breast cancer histological slide images based on stiffness matrix and feature fusion. Current Bioinformatics 10(4):476–493, 2015
Otsu N: A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man and Cybernetics 9(1):62–66, 1979
Bennett J, Catovsky D, Daniel M, Flandrin G, Galton D, Gralnick H, Sultan C: Proposals for the classification of the acute Leukaemias French-American-British (FAB) co-operative group. British journal of haematology 33(4):451–458, 1976
O. Sarrafzadeh, H. Rabbani, A. M. Dehnavi and A. Talebi, Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation, in Proc. IEEE ICIP, Quebec City, 2015.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Moshavash, Z., Danyali, H. & Helfroush, M.S. An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images. J Digit Imaging 31, 702–717 (2018). https://doi.org/10.1007/s10278-018-0074-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10278-018-0074-y