Analysis of white blood cell differential counts using dual-tree complex wavelet transform and support vector machine classifier

Abstract

A widely used pathological screening test for blood smears is the complete blood count which classifies and counts peripheral particles into their various types. We particularly interested in the classification and counting of the five main types of white blood cells (leukocytes) in a clinical setting where the quality of microscopic imagery may be poor. A critical first step in the medical analysis of cytological images of thin blood smears is the segmentation of individual cells. The quality of the segmentation has a great influence on the cell type identification, but for poor quality, noisy, and/or low resolution images, segmentation is correspondingly less reliable. In this paper, we compensate for less accurate segmentation by extracting features based on wavelets using the Dual-Tree Complex Wavelet Transform (DT-CWT) which is based on multi-resolution characteristics of the image. These features then form the basis of classification of white blood cells into their five primary types with a Support Vector Machine (SVM) that performs classification by constructing hyper-planes in a high multi-dimensional space that separates cases of different classes. This approach was validated with experiments conducted on poor quality, normal blood smear images. © 2012 Springer-Verlag Berlin Heidelberg.