Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography.
Artif Intell Med
; 50(1): 13-21, 2010 Sep.
Article
em En
| MEDLINE
| ID: mdl-20547044
ABSTRACT
OBJECTIVE:
We investigate the influence of the clinical context of high-resolution computed tomography (HRCT) images of the chest on tissue classification. METHODS AND MATERIALS 2D regions of interest in HRCT axial slices from patients affected with an interstitial lung disease are automatically classified into five classes of lung tissue. Relevance of the clinical parameters is studied before fusing them with visual attributes. Two multimedia fusion techniques are compared early versus late fusion. Early fusion concatenates features in one single vector, yielding a true multimedia feature space. Late fusion consisting of the combination of the probability outputs of two support vector machines. RESULTS ANDCONCLUSION:
The late fusion scheme allowed a maximum of 84% correct predictions of testing instances among the five classes of lung tissue. This represents a significant improvement of 10% compared to a pure visual-based classification. Moreover, the late fusion scheme showed high robustness to the number of clinical parameters used, which suggests that it is appropriate for mining clinical attributes with missing values in clinical routine.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Informática Médica
/
Inteligência Artificial
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Interpretação de Imagem Radiográfica Assistida por Computador
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Tomografia Computadorizada por Raios X
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Técnicas de Apoio para a Decisão
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Doenças Pulmonares Intersticiais
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Sistemas de Apoio a Decisões Clínicas
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Pulmão
Tipo de estudo:
Observational_studies
/
Prognostic_studies
Limite:
Adult
/
Aged
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Aged80
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Female
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Humans
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Male
/
Middle aged
Idioma:
En
Revista:
Artif Intell Med
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2010
Tipo de documento:
Article
País de afiliação:
Suíça