Analysis of infected blood cell images using morphological operators

This work describes a system for detecting and classifying malaria parasites in images of Giemsa stained blood slides in order to evaluate the parasitaemia of the blood. The first aim of our system is to detect the parasites by means of an automatic thresholding based on a morphological approach. A major requirement of the whole system is an efficient method to segment cell images. So the paper also introduces a morphological approach to cell image segmentation, that is, more accurate than the classical watershed-based algorithm. We have applied grey scale granulometries based on opening with disk-shaped elements, flat and hemispherical. We have used a hemispherical disk-shaped structuring element to enhance the roundness and the compactness of the red cells improving the accuracy of the classical watershed algorithm, while we have used a disk-shaped flat structuring element to separate overlapping cells. These methods make use of knowledge of the fed blood cell structure, that is, not used in existing watershed-based algorithms. The last step of the system is classifying the parasites: we present two different classification methods, one based on morphological operators and another one based on colour histogram similarity. The framework is described with the help of a running example and then validated against 'expert' analysis of several more images. (C) 2002 Elsevier Science B.V. All rights reserved.