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The long-term goal of this project is to quantify the aeration of lung parenchyma in 3D CT scans of patients with acute respiratory distress syndrome. This task requires lung delineation, as well as elimination of airways and vessels. The objective of this article was to present and evaluate the method used to segment out the vascular trees.Vascular trees are segmented by variational region growing...
In this paper, we propose two solutions to integrate shape prior in a segmentation process based on region growing. Our special region growing algorithm relies upon a variational framework which allows to easily take into account shape prior in the segmentation process. Region growing is described as an optimization process that aims to minimize some special energy combining intensity function and...
Bone fragility involved in diseases such as osteoporosis implicates many mechanisms at the cellular level. It was recently shown that the lacunar-canalicular network interconnecting osteocytes has a major role in mechanosensitivity. So far, this system has only been studied from 2D microscopic images. In a previous work, we demonstrated the feasibility of synchrotron radiation micro-CT with a voxel...
Region growing has become a popular method for 3D segmentation. Starting from a seed, this approach allows one to extract a region by merging all its neighbors and comparing the extracted region to a reference. Here, we present an alternative approach to constrain the evolution of the region growing method in respect to a fixed reference shape. This approach is based on a shape description by the...
We propose an automated region growing integrating adaptive shape prior in order to segment biomedical images. In our work, the segmentation method is improved by taking into account a shape reference model by non-linear way. Thus, the proposed method is driven by statistical data computed from the evolving region and by a priori shape information given by the model. An improvement of the method is...
We propose a new robust adaptive region growing method (RoAd RG) based on two local parameters: the local mean value of the intensity function and the local mean value of the norm of the intensity gradient. This approach enables a better spread of the region growing inside the region of interest while avoiding the merge of outlier pixels. We tested our method on a synthesized noisy image, and demonstrated...
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