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Modelo de Contorno Ativo Crisp: nova técnica de segmentação dos pulmões em imagens de TC

2011; SciELO; Volume: 27; Issue: 4 Linguagem: Inglês

10.4322/rbeb.2011.021

1984-7750

Pedro P. Rebouças Filho, Paulo César Cortez, Marcelo Alcântara Holanda,

Radiomics and Machine Learning in Medical Imaging

This paper proposes a new Active Contour Model (ACM), called ACM Crisp, and evaluates the segmentation of lungs in computed tomography (CT) images. An ACM draws a curve around or within the object of interest. This curve changes its shape, when some energy acts on it and moves towards the edges of the object. This process is performed by successive iterations of minimization of a given energy, associated with the curve. The ACMs described in the literature have limitations when used for segmentations of CT lung images. The ACM Crisp model overcomes these limitations, since it proposes automatic initiation and new external energy based on rules and radiological pulmonary densities. The paper compares other ACMs with the proposed method, which is shown to be superior. In order to validate the algorithm a medical expert in the field of Pulmonology of the Walter Cantidio University Hospital from the Federal University of Ceara carried out a qualitative analysis. In these analyses 100 CT lung images were used. The segmentation efficiency was evaluated into 5 categories with the following results for the ACM Crisp: 73% excellent, without errors, 20% acceptable, with small errors, and 7% reasonable, with large errors, 0% poor, covering only a small part of the lung, and 0% very bad, making a totally incorrect segmentation. In conclusion the ACM Crisp is considered a useful algorithm to segment CT lung images, and with potential to integrate medical diagnosis systems.