2 research outputs found
Knowledge-Based Deformable Surface Model with Application to Segmentation of Brain Structures in MRI
We have developed a knowledge-based deformable surface for segmentation of medical images. This work has been done in the
context of segmentation of hippocampus from brain MRI, due to its challenge and clinical importance. The model has a
polyhedral discrete structure and is initialized automatically by analyzing brain MRI sliced by slice, and finding few landmark
features at each slice using an expert system. The expert system decides on the presence of the hippocampus and its general
location in each slice. The landmarks found are connected together by a triangulation method, to generate a closed initial surface.
The surface deforms under defined internal and external force terms thereafter, to generate an accurate and reproducible boundary
for the hippocampus. The anterior and posterior (AP) limits of the hippocampus is estimated by automatic analysis of the location
of brain stem, and some of the features extracted in the initialization process. These data are combined together with a priori
knowledge using Bayes method to estimate a probability density function (pdf) for the length of the structure in sagittal direction.
The hippocampus AP limits are found by optimizing this pdf. The model is tested on real clinical data and the results show very
good model performance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85930/1/Fessler166.pd
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. (C) 1999 Published by Elsevier Science B.V. All rights reserved