Disjunctive normal shape models
Ramesh, Nisha and Mesadi, Fitsum and Çetin, Müjdat and Taşdizen, Tolga (2015) Disjunctive normal shape models. In: 12th International Symposium on Biomedical Imaging (ISBI 2015), New York, NY
This is the latest version of this item.
Official URL: http://dx.doi.org/ 10.1109/ISBI.2015.7164170
A novel implicit parametric shape model is proposed for segmentation and analysis of medical images. Functions representing the shape of an object can be approximated as a union of N polytopes. Each polytope is obtained by the intersection of M half-spaces. The shape function can be approximated as a disjunction of conjunctions, using the disjunctive normal form. The shape model is initialized using seed points defined by the user. We define a cost function based on the Chan-Vese energy functional. The model is differentiable, hence, gradient based optimization algorithms are used to find the model parameters.
Available Versions of this Item
Repository Staff Only: item control page