Area identification of bone marrow smears using radial-basis function networks and the HSI colour model

Reports on the results of a study using neural networks and the HSI (hue, saturation and intensity) colour model for the identification of areas, suitable for further image processing, from bone marrow smears. 25 μm² areas of the image were sparse sampled and this acted as the input to the neural networks. The classification ability of multi-layer perceptron (MLP) networks and radial basis function (RBF) networks were compared and it is was found that RBF networks proved to be superior for this task. It was also noted that the saturation plane was the least useful for the differentiation of suitable areas. By using the system and scanning the image on a pixel by pixel basis it was possible to produce `masks´ which identified areas worthy of further processing