  Analysis of microscopy images is one important tool in many fields of
biomedical research, as it allows the quantification of a multitude of
parameters at the cellular level. However, manual counting of these images is
both tiring and unreliable and ultimately very time-consuming for biomedical
researchers. Not only does this slow down the overall research process, it also
introduces counting errors due to a lack of objectivity and consistency
inherent to the researchers own human nature.
  This thesis addresses this issue by automatically determining infection
indexes of macrophages parasite by Leishmania in microscopy images using
computer vision and pattern recognition methodologies. Initially images are
submitted to a pre-processing stage that consists in a normalization of
illumination conditions. Three algorithms are then applied in parallel to each
image. Algorithm A intends to detect macrophage nuclei and consists of
segmentation via adaptive multi-threshold, and classification of resulting
regions using a set of collected features. Algorithm B intends to detect
parasites and is similar to Algorithm A but the adaptive multi-threshold is
parameterized with a different constraints vector. Algorithm C intends to
detect the macrophages and parasites cytoplasm and consists of a cut-off
version of the previous two algorithms, where the classification step is
skipped. Regions with multiple nuclei or parasites are processed by a voting
system that employs both a Support Vector Machine and a set of region features
for determining the number of objects present in each region. The previous vote
is then taken into account as the number of mixtures to be used in a Gaussian
Mixture Model to decluster the said region. Finally each parasite is assigned
to, at most, a single macrophage using minimum Euclidean distance to a cell
nucleus, thus quantifying Leishmania infection levels.
