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Detection of AML in Blood Microscopic Images using Local Binary Pattern and Supervised Classifier


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1 ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, India
     

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A novel method of detecting Acute myelogenous leukemia (AML) disease using image processing algorithms is discussed in this paper. AML is an high risk disease which should be diagnosed early. AML detection is challenging, and should be performed by a qualified hematopathologist or hematologist. This paper discusses an automatic detection of AML using image processing methods. The algorithm consists contrast enhancement, Local binary pattern detection and Fuzzy C mean clustering technique. This Automatic detection method will helps the hematologists for easier identification and early detection of leukemia from blood microscopic images which will improve the chances of survival for the patient. A fuzzy based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia.

Keywords

Acute Myelogenous Leukemia, Fuzzy C Mean Clustering, Local Binary Pattern Feature Extraction.
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  • Lorenzo Putzua, Giovanni Caoccib, Cecilia Di Rubertoa. Leucocyte classification for leukaemia detection using image processing techniques. Artificial Intelligence in Medicine vol no 62 (2014) pp 179–191.

  • Madhloom HT, Kareem SA, Ariffin H, Zaidan AA, Alanazi HO, Zaidan BB. An automated white blood cell nucleus localization and segmentation using image arithmetic and automated threshold. J Appl Sci 2010;10(11):959–66.

  • Sinha N, Ramakrishnan AG. Automation of differential blood count. In: Chockalingam A. Proceedings of the conference on convergent technologies for the Asia-Pacific region, October 15–17. IEEE Publisher; 2003. p. 547–51.

  • Kovalev VA, Grigoriev AY, Ahn H. Robust recognition of white blood cell images. In: Kavanaugh ME, Werner B. Proceedings of the 13th international conference on pattern recognition, August 25–29. Vienna, Austria: IEEE Publisher; 1996. p. 371–5.

  • V.P. Ananthi, P. Balasubramaniam. A new thresholding technique based on fuzzy set as an application to leukocyte nucleus segmentation. computer methods and programs in biomedicine 134, 2016, pp 165-177.

  • S. Chinwaraphat, A. Sanpanich, C. Pintavirooj, M. Sangworasil, P. Tosranon. A modified fuzzy clustering for white blood cell segmentation, in: 3rd International Symposium on Biomedical Engineering, 2008, pp. 356–359.

  • Jie Su, Shuai Liu , Jinming Song. A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia. Computer Methods and Programs in Biomedicine 152 (2017) 115–123.

  • Omid Sarrafzadeh, Hossein Rabbani, Alireza Mehri Dehnavi, Ardeshir Talebi. Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation. IEEE International Conference on Image Processing (ICIP), 2015, pp 3339 – 3343.

  • E. Montseny, P. Sobrevilla, and S. Romaní. A fuzzy approach to white blood cells segmentation in color bone marrow images. Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on, 2004, pp. 173-178.

  • D. M. U. Sabino, L. da Fontoura Costa, E. Gil Rizzatti, and M. Antonio Zago. A texture approach to leukocyte recognition. RealTime Imaging, vol. 10, pp. 205-216, 2004.

  • N. Theera-Umpon, S. Dhompongsa. Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification. IEEE Trans. Inf. Technol. Biomed. 11 (3) (2007) 353–359.

  • Xuming Zhang and Youlun Xiong. Impulse Noise Removal Using Directional Difference Based Noise Detector and Adaptive Weighted Mean Filter. IEEE signal processing letters, vol. 16, no. 4, pp.295-298, April 2009.

  • Yiqiu Dong and Shufang Xu. A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise. IEEE signal processing letters, vol. 14, no. 3, pp. 193-196 , March 2007.

  • T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002.

  • J. C. Bezdek. Pattern Recognition With Fuzzy Objective Function Algorithms. New York, NY, USA: Plenum, 1981.

  • Yuchun Tang, Yan-Qing Zhang. FCM-SVM-RFE Gene Feature Selection Algorithm for Leukemia Classification from Microarray Gene Expression Data. The IEEE International Conference on Fuzzy Systems, 2005, pp 99-101.

  • P.Chitra. Quantitative Characterization of Radiographic Weld Defect Based on the Ground Truth Radiographs Made on a Stainless Steel Plates. Advances in Intelligent Systems and Computing, Springer publication, Volume 433, pp 157-166,2016.

  • Melissa, S., Srilatha, K. A novel approach for pigmented epidermis layer segmentation and classification. International Journal of Pharmacy and Technology. March-2016, Vol. 8, Issue No.1, 10449-10458.

  • S. Sheela, M. Sumathi. Study and Theoretical Analysis of Various Segmentation Techniques for Ultrasound Images. ICRTCSE 2016, Elsevier – Procedia Computer Science, no.87, pp. 67 – 73, 2016.


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  • Detection of AML in Blood Microscopic Images using Local Binary Pattern and Supervised Classifier

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Authors

P. Chitra
ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, India
M. R. Ebenezer Jebarani
ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, India
P. Kavipriya
ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, India
K. Srilatha
ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, India
M. Sumathi
ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, India
S. Lakshmi
ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, India

Abstract


A novel method of detecting Acute myelogenous leukemia (AML) disease using image processing algorithms is discussed in this paper. AML is an high risk disease which should be diagnosed early. AML detection is challenging, and should be performed by a qualified hematopathologist or hematologist. This paper discusses an automatic detection of AML using image processing methods. The algorithm consists contrast enhancement, Local binary pattern detection and Fuzzy C mean clustering technique. This Automatic detection method will helps the hematologists for easier identification and early detection of leukemia from blood microscopic images which will improve the chances of survival for the patient. A fuzzy based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia.

Keywords


Acute Myelogenous Leukemia, Fuzzy C Mean Clustering, Local Binary Pattern Feature Extraction.

References