Dissimilarity-based classification of chromatographic profiles


This paper proposes a non-parametric method for the classification of thin-layer chromatographic (TLC) images from patterns represented in a dissimilarity space. Each pattern corresponds to a mixture of Gaussian approximation of the intensity profile. The methodology comprises various phases, including image processing and analysis steps to extract the chromatographic profiles and a classification phase to discriminate among two groups, one corresponding to normal cases and the other to three pathological classes. We present an extensive study of several dissimilarity-based approaches analysing the influence of the dissimilarity measure and the prototype selection method on the classification performance. The main conclusions of this paper are that, Match and Profile-difference dissimilarity measures present better results, and a new prototype selection methodology achieves a performance similar or even better than conventional methods. Furthermore, we also concluded that simplest classifiers, such as k-NN and linear discriminant classifiers (LDCs), present good performance being the overall classification error less than 10% for the four-class problem.

DOI: 10.1007/s10044-008-0113-2

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@article{Sousa2008DissimilaritybasedCO, title={Dissimilarity-based classification of chromatographic profiles}, author={Ant{\'o}nio V. Sousa and Ana Maria Mendonça and Aur{\'e}lio J. C. Campilho}, journal={Pattern Analysis and Applications}, year={2008}, volume={11}, pages={409-423} }