Abstract
In several applications involving medical image analysis, the process of image segmentation, be it automatic or manual, is a present task. An accurate segmentation provides information for inspection of anatomical structures, to identify diseases and monitoring of its progress, and even for surgical planning and simulation. Thus, the role of image segmentation is essential in any medical image analysis system. Among the segmentation techniques in the literature, the active models technique is one of the most popular approaches of the last two decades and has been widely used in medical image segmentation, achieving considerable success. Active models that are applied on three-dimensional applications are called Active Surfaces Methods (ASM), which has been widely used in the segmentation of 3D objects, evolving under the influence of their energy to converge to the desired surface. So, knowing how essential surface extraction is to obtain an accurate segmentation, this paper proposes an automatic initialization method for ASM applied in lobes segmentation in CT images. The proposed method has achieved significant results, with overall accuracy rate of 94%.
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da Silveira Cavalcante, T., Cortez, P.C., Ribeiro, A.B.N., Neto, E.C., Rodrigues, V.E., de Almeida, T.M. (2019). Automatic Initialization of 3D Active Models for Lobe Segmentation in Thorax CT Images. In: Costa-Felix, R., Machado, J., Alvarenga, A. (eds) XXVI Brazilian Congress on Biomedical Engineering. IFMBE Proceedings, vol 70/2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2517-5_11
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DOI: https://doi.org/10.1007/978-981-13-2517-5_11
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