Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a
range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for
parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based
diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the
field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved
by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the
diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during
the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood
sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by
a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After
subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a
certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false).
As such the computer program should be able to ”predict” with reasonable level of confidence if a given particle
belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three
continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a
logistic regression equation as they proved to have a relatively high predictive value on their own
