This paper proposes image processing algorithms to recognize five types of white blood
cells in peripheral blood automatically. First, a method based on Gram–Schmidt
orthogonalization is proposed along with a snake algorithm to segment nucleus and
cytoplasm of the cells. Then, a variety of features are extracted from the segmented regions.
Next, most discriminative features are selected using a Sequential Forward Selection (SFS)
algorithm and performances of two classifiers, Artificial Neural Network (ANN) and Support …

An automatic system which is capable of recognizing white blood cells can assist
hematologists in the diagnosis of many diseases. In this paper, we propose a new system
based on image processing techniques in order to recognize five types of white blood cells
in the peripheral blood. To segment nucleus and cytoplasm, a Gram-Schmidt
orthogonalization method and a snake algorithm are applied, respectively. Moreover, three
kinds of features are extracted from the segmented areas and two groups of textural features …