Dissertação
Uma técnica otimizada de clusterização para segmentação de imagens de TC de tórax de alta-resolução
Autor
Porto, Marcelo Arrais
Institución
Resumen
Lung segmentation is a fundamental step in many image analysis applications for lung
diseases and abnormalities in thoracic computed tomography (CT). However, due to
the large variations in pathology that may be present in thoracic CT images, it is difficult
to extract the lung regions accurately, especially when the lung parenchyma contains
extensive lung diseases. A major insight to deal with this problem is the existence
of new approaches to cope with quality and performance. This paper presents an
optimized superpixel clustering approach for high-resolution chest CT segmentation.
The proposed algorithm is compared against some open source superpixel algorithms
while a performance evaluation is carried out in terms of boundary recall and undersegmentation
error metrics. The over-segmentation results on a Computed Tomography
Emphysema Database demonstrates that our approach shows better performance
than other three state-of-the-art superpixel methods.