Optimal Edge Detection using Expansion Matching and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Encoding of a priori Information in Active Contour Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A snake for model-based segmentation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing
Visualization of tooth for 3-d simulation
AsiaSim'04 Proceedings of the Third Asian simulation conference on Systems Modeling and Simulation: theory and applications
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Automatic segmentation of MR images is a complex task, particularly for structures which are barely visible on MR. Hippocampus is one of such structures. We present an active contour based segmentation algorithm, suited to badly defined structures, and test it on 8 hippocampi. The basic algorithm principle could also be applied for object tracking on movie sequences. Algorithm initialisation consists of manual segmentation of some key images. We discuss and solve numerous problems: partially blurred or discontinuous object boundaries; low image contrasts and S/N ratios; multiple distracting edges, surrounding the correct object boundaries. The active contours' inherent limitations were overcome by encoding a priori geometric information into the deformation algorithm. We present a geometry encoding algorithm, followed by specializations needed for hippocampus segmentation. We validate the algorithm by segmenting normal and atrophic hippocampi. We achieve volumetric errors in the same range as those of manual segmentation (±5%). We also evaluate the results by false positive/negative errors and relative amounts of volume agreements.