Automated white blood cell classification revisited

Abstract

A novel approach to the problem of automated white blood cell classification is described. Whereas in most earlier attempts the segmentation of the cells has been recognized as the most difficult and most critical step in the sequence of operations resulting in the classification, the method described here eliminates the necessity of the detection of the contour of the nucleus and of the cytoplasm, and is therefore less sensitive to such disturbing factors as the presence of granules, of other cells touching the cell of interest, etc. The multiple sequential threshold method to be described here in two slightly different variants yields a correct classification rate of 94.7% for a 4 class problem (90 cells in the test set), and 91.8% for an 8 class problem (279 cells in the test set). Both experiments include immature cell types.

Key Words:

• White blood cells
• Segmentation
• Automated cytology
• Pattern recognition