Dissimilarity-based classification of chromatographic profiles

• Tipo: Artigo em Revista Científica Internacional
• Data: 2008

Autores

Antonio V Sousa (Autor) Outra; Ana Maria Mendonca (Autor) FEUP; Aurelio Campilho (Autor) FEUP

Revista

Título: Pattern Analysis and Applications

Vol. 11 Nº 3

Páginas: 409-423

ISSN: 1433-7541

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 5 Citações

ISI Web of Science

Scopus - 6 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Tecnológicas > Tecnologia > Tecnologia de computadores > Processamento de imagem

Outras Informações

ID Authenticus: P-003-WRQ

DOI: 10.1007/s10044-008-0113-2

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15