RECOGNITION AND COUNTING METHOD OF MAMMALIAN CELLS ON MICRO·CARRIER USING IMAGE PROCESSING AND NEURAL NETWORK

This paper presents a method for recognizing mammalian cells (fibroblast) on micro-carrier (bead; bead is sphere-shaped and transparent) using a technique of image processing and neural network. The cell culture on micro-carriers is one of the efficient cell culture methods. It is an important task to monitor optimal culture conditions such as the growth rate of cells on a bead. A system for counting the number of cells will provide an automatic production of cells. Previous studies presented the counting algorithms using image segmentation, knowledge data base systems and fuzzy inference. These algorithms are incomplete, because they cannot recognize adjoining cells and overlapping cells which are on the top and the bottom part of a bead. In this paper, we propose a new cell counting system, which is developed using image processing and neural network and considers the nucleoli in the cell. Experimental results show the realization of counting overlapping cells and adjoining cells.