A robust cell counting approach based on a normalized 2D cross-correlation scheme for in-line holographic images.

To achieve the important aims of identifying and marking disease progression, cell counting is crucial for various biological and medical procedures, especially in a Point-Of-Care (POC) setting. In contrast to the conventional manual method of counting cells, a software-based approach provides improved reliability, faster speeds, and greater ease of use. We present a novel software-based approach to count in-line holographic cell images using the calculation of a normalized 2D cross-correlation. This enables fast, computationally-efficient pattern matching between a set of cell library images and the test image. Our evaluation results show that the proposed system is capable of quickly counting cells whilst reliably and accurately following human counting capability. Our novel approach is 5760 times faster than manual counting and provides at least 68% improved accuracy compared to other image processing algorithms.