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6 July 2015 Efficient threshold for volumetric segmentation
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Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96310C (2015)
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States
Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.
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Dumitru Dan Burdescu, Marius Brezovan, Liana Stanescu, Cosmin Stoica Spahiu, and Daniel Ebanca "Efficient threshold for volumetric segmentation", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96310C (6 July 2015);

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