Authors:
Verónica Vasconcelos
1
;
Luis Marques
2
;
João Barroso
3
and
José Silvestre Silva
4
Affiliations:
1
Instituto Superior de Engenharia, Instituto Politécnico de Coimbra and Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Portugal
;
2
Instituto Superior de Engenharia and Instituto Politécnico de Coimbra, Portugal
;
3
Universidade de Trás-os-Montes e Alto Douro, Portugal
;
4
Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Portugal
Keyword(s):
Statistical texture analysis, Support vector machines, Pulmonary emphysema, High-resolution computed tomography.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Digital Image Processing
;
Image and Video Analysis
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
Abstract:
High-resolution computed tomography (HRCT) became an essential tool in detection, characterization and follow -up of lung diseases. In this paper we focus on lung emphysema, a long-term and progressive disease characterized by the destruction of lung tissue. The lung patterns are represented by different features vectors, extracted from statistical texture analysis methods (spatial gray level dependence, gray level run-length method and gray level difference method). Support vector machine (SVM) was trained to discriminate regions of healthy lung tissue from emphysematous regions. The SVM model optimization was performed in the training dataset through a cross validation methodology, along a grid search. Three usual kernel functions were tested in each of the features sets. This study highlights the importance of the kernel choice and parameters tuning to obtain models that allow high level performance of the SVM classifier.