A Feature Extraction Method Based on Morphological Operators for Automatic Classification of Leukocytes

In this paper we present preliminary results obtained from the application of morphological operator pecstrum, for the extraction of discriminating characteristics in leukocytes and similar artificial images. Experts have identified six categories of leukocytes, very similar in shape and size, which makes them extremely difficult to distinguish automatically or even by non-expert humans. A feature vector based on a 7-component pecstrum, normalized area, and nucleus-cytoplasm area ratio, was tested using 4 kinds of recognizers: Euclidean distance, k-nearest neighbor, back propagation neural net and support vector machine. Using 36 patterns for training and 18 for testing, recognition of 87% was obtained in the best case, which is encouraging, given the complexity of the problem. The amount of samples used at this point for experiments is not statistically representative, however these results are promising and more experiments will be carried out.