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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.

has been cited by the following article:

  • TITLE: A Combination of Feature Selection and Co-occurrence Matrix Methods for Leukocyte Recognition System

    AUTHORS: Li Na, Arlends Chris, Bagus Mulyawan

    KEYWORDS: Leukocyte recognition; White blood cell; Microscopic image; Feature selection;Co-occurrence matrix

    JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.12B, January 23, 2013

    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.