A Geodesic Active Contour Level Set Method for Image Segmentation

K.R. Ananth, S. Pannirselvam
2012 International Journal of Image Graphics and Signal Processing  
Image segmentation is a vital part of many applications because it makes possible for the information extraction and analysis of image contents. To enclose the detected object, contour is used and its deformable models intends at making this contour change so that it matches the edge of the considered object. The contour has taken the object for the entire which is related to an energy-minimization problem. The energy functional is defined as a weighted mixture of both internal forces (relating
more » ... al forces (relating properties of elasticity and rigidity of the contour) and external forces (that show the curve towards the boundary of the object). The Geodesic active contour (GAC) approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The main properties of GAC are 1) Illustrates the relation between energy and curve progress approaches of active contours. 2) Presents dynamic curve for object detection as a GAC approach. 3) Expand existing curve progression models as a result of the GAC formulation. 4) Permit concurrent recognition of interior and exterior boundaries in numerous objects without unique contour tracking procedures. 5) Holds proper existence, individuality, firmness, and reliability results. 6) Does not need particular stopping conditions. The topological flexibility is a great benefit since it permits the concurrent discovery of numerous objects in the image, which was impossible in the case of parametric deformable models. In brain imaging, for example, when situated near a lobe, parts of the shape may wrinkle and at last have a contact point (without
doi:10.5815/ijigsp.2012.05.04 fatcat:weejjm3nlra4lkboavhan56loq