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.
Implicit parametric shape models, namely, disjunctive normal shape models, are used to learn representations of cells accurately, where the time complexity increases with the density of the images REF .
Disjunctive normal shape models
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
Medicine Mathematics Computer Science