20 March 2014 Selection of the best features for leukocytes classification in blood smear microscopic images
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Abstract
Automatic differential counting of leukocytes provides invaluable information to pathologist for diagnosis and treatment of many diseases. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and classify them into their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte using features that pathologists consider to differentiate leukocytes. Features contain color, geometric and texture features. Colors of nucleus and cytoplasm vary among the leukocytes. Lymphocytes have single, large, round or oval and Monocytes have singular convoluted shape nucleus. Nucleus of Eosinophils is divided into 2 segments and nucleus of Neutrophils into 2 to 5 segments. Lymphocytes often have no granules, Monocytes have tiny granules, Neutrophils have fine granules and Eosinophils have large granules in cytoplasm. Six color features is extracted from both nucleus and cytoplasm, 6 geometric features only from nucleus and 6 statistical features and 7 moment invariants features only from cytoplasm of leukocytes. These features are fed to support vector machine (SVM) classifiers with one to one architecture. The results obtained by applying the proposed method on blood smear microscopic image of 10 patients including 149 white blood cells (WBCs) indicate that correct rate for all classifiers are above 93% which is in a higher level in comparison with previous literatures.
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Omid Sarrafzadeh, Omid Sarrafzadeh, Hossein Rabbani, Hossein Rabbani, Ardeshir Talebi, Ardeshir Talebi, Hossein Usefi Banaem, Hossein Usefi Banaem, } "Selection of the best features for leukocytes classification in blood smear microscopic images", Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410P (20 March 2014); doi: 10.1117/12.2043605; https://doi.org/10.1117/12.2043605
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