M. Soriano et C. Saloma, IMPROVED CLASSIFICATION ROBUSTNESS FOR NOISY CELL IMAGES REPRESENTED AS PRINCIPAL-COMPONENT PROJECTIONS IN A HYBRID RECOGNITION SYSTEM, Applied optics, 37(17), 1998, pp. 3628-3638
Different types of cells are recognized from their noisy images by use
of a hybrid recognition system that consists of a learning principal-
component analyzer and an image-classifier network. The inputs to the
feed-forward backpropagation classifier are the first 15 principal com
ponents of the 10 x 10 pixel image to be classified. The classifier wa
s trained with clear images of cells in metaphase, unburst cells, and
other erroneous patterns. Experimental results show that the recogniti
on system is robust to image scaling and rotation, as well as to image
noise. Cell recognition is demonstrated for images that are corrupted
with additive Gaussian noise, impulse noise, and quantization errors.
We compare the performance of the hybrid recognition system with that
of a conventional three-layer feed-forward backpropagation network th
at uses the raw image directly as input. (C) 1998 Optical Society of A
merica.