Geometrically Induced Force Interaction for Three-Dimensional Deformable Models

This work introduces a novel 3D deformable model that is based on a geometrically induced external force field, which can be conveniently generalised to arbitrary dimensions. This external force field is based on hypothesised interactions between the relative geometries of the deformable model and the object boundary. The relative geometrical configurations contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge preserving algorithm, the new model can effectively overcome image noise. We provide a comprehensive comparative study and show that the proposed method achieves significant improvements against existing techniques

Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing

The hippocampus has been known as one of the most important structures referred to as Alzheimer’s disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists

Active Contour Model for Image Segmentation with Dilated Convolution Filter

ACMs have been demonstrated to be highly suitable as image segmentation models for computer vision tasks. Among other ACM, the local region-based models show better performance because they extract the local information regarding intensity in the neighborhood and embed it into the energy minimization function to guide the active contour to the boundary of the desired object. However, the online segmentation of noisy and inhomogeneous is still a challenging task for local region-based ACM models. To overcome this challenge, the paper proposes a novel region-based active contour model, named active contour model with local dilated convolution filter (ACLD). The ACLD integrates local image information in the form of a signed pressure force function. Then, a Gaussian kernel is applied using dilated convolution instead of discrete convolution for regularizing the level set formulation. Finally, instead of using a constant stopping condition, the ACLD automatically stops at the object boundaries. The proposed model shows improved image segmentation results visually combined with less computational time in the case of synthetic and natural images compared with the state-of-the-art models. Further, on the ISIC2017 dataset, the ACLD yields segmentation results with the highest accuracy. </p