  The importance of organizing medical images according to their nature,
application and relevance is increasing. Furhermore, a previous selection of
medical images can be useful to accelerate the task of analysis by
pathologists. Herein this work we propose an image classifier to integrate a
CBIR (Content-Based Image Retrieval) selection system. This classifier is based
on pattern spectra and neural networks. Feature selection is performed using
pattern spectra and principal component analysis, whilst image classification
is based on multilayer perceptrons and a composition of self-organizing maps
and learning vector quantization. These methods were applied for content
selection of immunohistochemical images of placenta and newdeads lungs. Results
demonstrated that this approach can reach reasonable classification
performance.
