A multiclassifier approach for lung nodule classification

• Tipo: Artigo em Revista Científica Internacional
• Data: 2006

Autores

Carlos S Pereira (Autor) Outra; Luis A Alexandre (Autor) Outra; Ana Maria Mendonca (Autor) FEUP; Aurelio Campilho (Autor) FEUP

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: IMAGE ANALYSIS AND RECOGNITION, PT 2

Vol. 13

Páginas: 612-623

Indexação

ISI Proceedings

ISI Web of Knowledge - 8 Citações

ISI Web of Science

Scopus - 11 Citações

Classificação Científica

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

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia biomédica

Outras Informações

ID Authenticus: P-004-PXP

DOI: 10.1007/11867661_55

Abstract (EN): The aim of this paper is to examine a multiclassifier approach to the classification of the lung nodules in X-ray chest radiographs. The approach investigated here is based on an image region-based classification whose output is the information of the presence or absence of a nodule in an image region. The classification was made, essentially, in two steps: firstly, a set of rotation invariant features was extracted from the responses of a multi-scale and multi-orientation filter bank; secondly, different classifiers (multi-layer perceptrons) are designed using different features sets and trained in different data. These classifiers are further combined in order to improve the classification performance. The obtained results are promising and can be used for reducing the false-positives nodules detected in a computer-aided diagnosis system.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12