High Throughput Algorithm for Leukemia Cell Population Statistics on a Hemocytometer

Abstract

This paper presents a high throughput cell count and cluster classification algorithm to quantify population statistics of leukemia cell lines on a conventional hemocytometer. The algorithm has been designed, implemented and tested on test images that vary in image quality. The proposed algorithm uses a recursively segmented, median filtered and a boosted Prewitt gradient mask to generate a boundary box that encloses all the identified cells. Intensity profile plots acting as signature plots further assist in classifying a single isolated cell from a cell cluster. Processed results compared manually by a biological expert resulted in an accuracy of 95 % for even low quality images with a computational time ranging between 8-12sec. Improved performance from the proposed algorithm could be observed when compared with other conventional image analysis tools.