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

• Autor(a) principal: Jodas, Dinilo Samuel
• Data de Publicação: 2017
• Outros Autores: Pereira, Aledir Silveira [UNESP], Tavares, Joao Manuel R. S.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.media.2017.06.006; http://hdl.handle.net/11449/165700
• topic: Medical imaging; Intravascular ultrasound; Image pre-processing; Image segmentation

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 the identification 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 the K-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 area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 +/- 0.06, 0.29 +/- 0.17 mm, 0.09 +/- 0.07 and 0.94 +/- 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction. (C) 2017 Elsevier B.V. All rights reserved.