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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning

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Autor(es):
Macedo, Maysa M. G. [1] ; Guimaraes, Welingson V. N. [2] ; Galon, Micheli Z. [2] ; Takimura, Celso K. [2] ; Lemos, Pedro A. [2] ; Gutierrez, Marco Antonio [1]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Sch Med, Heart Inst InCor, Div Informat, BR-05403900 Sao Paulo - Brazil
[2] Univ Sao Paulo, Sch Med, Heart Inst InCor, Hemodynam, BR-05403900 Sao Paulo - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: Computerized Medical Imaging and Graphics; v. 46, n. 2, SI, p. 237-248, DEC 2015.
Citações Web of Science: 5
Resumo

Intravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intravascular introduction of a catheter which provides a view of the inner wall of blood vessels with a spatial resolution of 10-20 mu m. Recent studies in IV-OCT have demonstrated the importance of the bifurcation regions. Therefore, the development of an automated tool to classify hundreds of coronary OCT frames as bifurcation or nonbifurcation can be an important step to improve automated methods for atherosclerotic plaques quantification, stent analysis and co-registration between different modalities. This paper describes a fully automated method to identify IV-OCT frames in bifurcation regions. The method is divided into lumen detection; feature extraction; and classification, providing a lumen area quantification, geometrical features of the cross-sectional lumen and labeled slices. This classification method is a combination of supervised machine learning algorithms and feature selection using orthogonal least squares methods. Training and tests were performed in sets with a maximum of 1460 human coronary OCT frames. The lumen segmentation achieved a mean difference of lumen area of 0.11 mm(2) compared with manual segmentation, and the AdaBoost classifier presented the best result reaching a F-measure score of 97.5% using 104 features. (C) 2015 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
Beneficiário:Roberto Marcondes Cesar Junior
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 13/09922-8 - Classificação automática de tecidos vasculares em imagens de tomografia por coerência óptica
Beneficiário:Maysa Malfiza Garcia de Macedo
Linha de fomento: Bolsas no Brasil - Pós-Doutorado