Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation
Image and Vision Computing
Spermatogonium image recognition using Zernike moments
Computer Methods and Programs in Biomedicine
Computers in Biology and Medicine
Multiclass detection of cells in multicontrast composite images
Computers in Biology and Medicine
Pattern Recognition and Image Analysis
Automatic recognition of five types of white blood cells in peripheral blood
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
A visual targeting system for the microinjection of unstained adherent cells
Computers in Biology and Medicine
Computers in Biology and Medicine
Hi-index | 0.00 |
In this paper, we describe a novel strategy for combining fisher's linear discriminant (FLD) preprocessing with a feedforward neural network to classify cultured cells in bright field images. This technique was applied to various experimental scenarios utilizing different imaging environments, and the results were compared with those for the traditional principal component analysis (PCA) preprocessing. Our FLD preprocessing was shown to be more effective than PCA due in large part to the fact that FLD maximizes the ratio of between-class to within-class scatter. The new cell recognition algorithm with FLD preprocessing improves accuracy while the speed is suitable for practical applications.