CELL RECOGNITION BASED ON PCA AND BAYESIAN CLASSIFICATION
Saeid Sanei, Tracey K. M. Lee · 2003
Recognition of the blood cell types is of great importance due to its high clinical diagnostic applications. Here, a method based on PCA followed by Bayesian classification, for identification of blood cells has been introduced. We have modified the work by Turk and Pentland on face recognition and extended it to cell recognition. Their method uses the standard method of eigenvector selection. Also only monochrome images have been considered and the method is not tolerant enough to geometrical changes. Here the idea has been extended to colour patterns. We pre-processe the images adjusting their size and rotation, with fast methods. The eigencells are selected based on minimisation of similarities among various sets and finally a classifier identifies the cell types by looking at the three-fold intensity-colour information. This overcomes many problems in cell classification where either certain cells are recognised or some constraints such as geometrical variations are incorporated.