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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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  • 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