Automatic segmentation of the lumen region in intravascular images of the coronary artery

• Autor(a) principal: Danilo Samuel Jodas
• Data de Publicação: 2017
• Outros Autores: Aledir Silveira Pereira, João Manuel R. S. Tavares
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/104656
• topic: Ciências Tecnológicas, Ciências médicas e da saúde; Technological sciences, Medical and Health sciences

Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and theidentification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on theK-means algorithm and the mean roundness to identify the region corresponding to the potential lumen.An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the areawith the potential lumen regions. Additionally, an active contour model is applied to refine the contourof the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed methodwere compared against manual delineations made by two experts in 326 IVUS images of the coronaryartery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference andDice coefficient were 0.88 ± 0.06, 0.29 ± 0.17 mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324IVUS images successfully segmented. Additionally, a comparison with the studies found in the literatureshowed that the proposed method is slight better than the majority of the related methods that havebeen proposed. Hence, the new automatic segmentation method is shown to be effective in detecting thelumen in IVUS images without using complex solutions and user interaction.