ABSTRACT A leukocyte recognition system, as part of a differential blood counter system, is very important in hematology field. In this paper, the propose system aims to automatically classify the white blood cells (leukocytes) on a given microscopic image. The classifications of leukocytes are performed based on the combination of color and texture features of the blood cell images. The developed system classifies the leukocytes in one of the five categories (neutrophils, eosinophils, basophils, lymphocytes, and monocytes). In the preprocessing stage, the system starts with converting the microscopic images from Red Green Blue (RGB) color space to Hue Saturation Value (HSV) color space. Next, the system splits the Hue and Saturation features from the Value feature. For both Hue and Saturation features, the system processes their color information using the Feature Selection method and the Window Cropping method; while the Value feature is processed by its texture information using the Co-occurrence matrix method. The final recognition stage is performed using the Euclidean distance method.The combination of the Feature Selection and Co-occurrence Matrix methods gives the best overall recognition accuracies for classifying leukocyte images.
Cite this paper
L. Na, A. Chris and B. Mulyawan, "A Combination of Feature Selection and Co-occurrence Matrix Methods for Leukocyte Recognition System," Journal of Software Engineering and Applications, Vol. 5 No. 12, 2012, pp. 101-106. doi: 10.4236/jsea.2012.512B020.
 M.M. Wintrobe, “Clinical Hematology,” 12th Edition, Lippincott Williams & Wilkins, Philadelphia, 2008.
 M. Adjouadi, N. Zong, and M. Ayala, “Mul-tidimensional Pattern Recognition and Classification of White Blood Cells using Support Vector Machines,” Particle & Particle Systems Characterization, Vol. 22, No. 2, 2005, pp. 107-118. doi: 10.1002/ppsc.200400888.
 T. Markiewicz and S. Osowski, “Data Mining Techniques for Feature Selec-tion in Blood Cell Recognition,” Proceedings of Euro-pean Symposium on Artificial Neural Networks, Belgium, 2006, pp. 407-412.
 M.C. Colunga, O.S. Siordia, S.J. Maybank, “Leukocyte Recognition using EM-algorithm,” Proceedings of 8th Mexican Internation-al Conference on Artificial Intelligence, Guanajuato, 2009, pp.545-555.
 N. Theera-Umpon and P.D. Gader, “Training Neural Networks to Count White Blood Cells via a Minimum Counting Error Objective Function,” Proceedings of International Conference on Pattern Recognition, Barcelona, 2000, pp.2299-2302.
 M. Beksac, M.S. Beksac, V.B. Tipi, H.A. Duru, M.U. Karakas, and A. Cakar,” An Artificial Intelligent Diagnostic System on Differential Recognition of Hematopoietic Cells from Microscopic Images,” Cytometry, Vol. 30, 1997, pp.145-150. doi: 10.1002/(SICI)1097-0320(19970615).
 A. Chris, S. Sugiharto, Lina,” Detection of Abnormalities of Lymph Node Tissues using Image Texture Analysis,” Proceed-ings of International Conference on Information Tech-nology and Applied Mathematics, Jakarta, 2012, pp.30-32.