  This paper proposes a novel automatic classification framework for the
recognition of five types of white blood cells. Segmenting complete white blood
cells from blood smears images and extracting advantageous features from them
remain challenging tasks in the classification of white blood cells. Therefore,
we present an adaptive threshold segmentation method to deal with blood smears
images with non-uniform color and uneven illumination, which is designed based
on color space information and threshold segmentation. Subsequently, after
successfully separating the white blood cell from the blood smear image, a
large number of nonlinear features including geometrical, color and texture
features are extracted. Nevertheless, redundant features can affect the
classification speed and efficiency, and in view of that, a feature selection
algorithm based on classification and regression trees (CART) is designed.
Through in-depth analysis of the nonlinear relationship between features, the
irrelevant and redundant features are successfully removed from the initial
nonlinear features. Afterwards, the selected prominent features are fed into
particle swarm optimization support vector machine (PSO-SVM) classifier to
recognize the types of the white blood cells. Finally, to evaluate the
performance of the proposed white blood cell classification methodology, we
build a white blood cell data set containing 500 blood smear images for
experiments. By comparing with the ground truth obtained manually, the proposed
segmentation method achieves an average of 95.98% and 97.57% dice similarity
for segmented nucleus and cell regions respectively. Furthermore, the proposed
methodology achieves 99.76% classification accuracy, which well demonstrates
its effectiveness.
