Volume estimation from sparse planar images using deformable models

In this article we will present Point Distribution Models (PDMs) constructed from Magnetic Resonance scanned foetal livers and will investigate their use in reconstructing 3D shapes from sparse data, as an aid to volume estimation. A solution of the model to data matching problem will be presented that is based on a hybrid Genetic Algorithm (GA). The GA has amongst its genetic operators, elements that extend the general Iterative Closest Point (ICP) algorithm to include deformable shape parameters. Results from using the GA to estimate volumes from two sparse sampling schemes will be presented. We will show how the algorithm can estimate liver volumes in the range of 10.26 to 28.84 cc with an accuracy of 0.17 ±4.44% when using only three sections through the liver volume.