Cell recognition by image processing and nonlinear cross correlation

Abstract

This work implements a novel hybrid method for detection and tracking of biological cells of "in vitro" samples (Goobic,¹ 2005). The method is able to detect and track cells based on image processing, nonlinear filters and normalized cross correlation (ncc) and it is tested on a full sequence of 1080 images of cell cultures. In addition of the cell speed, Cell tracking differentiate itself from tracking other kinds of tracking because cells show: mitosis, apthosis, overlapping and migration (Liao,² 1995). Image processing provides an excellent tool to improve cell recognition and background elimination, set as a priori task on this work and conveniently implemented by a Fourier analysis. The normal cross correlation was developed in the Fourier space to reduce time processing. The problem of the target detection was formulated as a nonlinear joint detection/estimation problem on the position parameters. A bank of spatially and temporally localized nonlinear filters is used to estimate the a posteriori likelihood of the existence of the target in a given space-time resolution cell. The shapes of the targets are random and according to the sequence, the targets change of shape almost every frame. However, the cross correlation result is based on the target shape matching, not in the position; and the system is invariant to rotation. Nonlinear filter makes a robust cell tracking method by producing a sharper correlation peak and reducing the false positives in the correlation. These false positives may also be reduced by using image preprocessing. Fourier and nonlinear filtering implementation showed the best results for the proposed cell tracking method presenting the best time consumption and the best cell localization.