A High Throughput Screening Algorithm for Leukemia Cells

This paper presents a high throughput screening algorithm for leukemia cells that has been designed, implemented, and tested. It performs a recursive image segmentation technique, row-wise and column-wise, on edge detected cell image. The recursive image segmentation successfully eliminates background pixels from foreground pixels by only segmenting image sections that contain relevant pixels. Then, the algorithm generates a boundary box for all identified cells. The next step of the proposed algorithm, the cluster classification, uses signature plots to classify single cells from cell clusters, and determine total cell count, size, and position. The proposed algorithm was successfully tested on various leukemia cell images. Also, when compared to manual counting using hemocytometer, the algorithm result matched the hemocytometer result. In addition, the algorithm took less than three seconds to process each image. Hence, the proposed algorithm determines relevant cell population statistics with 95% accuracy and avoids unnecessary delays in the cell screening process