A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Volumetric segmentation of medical images by three-dimensional bubbles
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dynamics of Nonlinear Relaxation Labeling Processes
Journal of Mathematical Imaging and Vision
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We propose a new technique to extract a pulmonary nodule from helical thoracic CT scans and estimate its diameter. The technique is based on a novel segmentation, or label-assignment, framework called competition-diffusion (CD), combined with robust ellipsoid fitting (EF). The competition force defined by replicator equations draws one dominant label at each voxel, and the diffusion force encourages spatial coherence in the segmentation map. CD is used to reliably extract foreground structures, and nodule like objects are further separated from attached structures using EF. Using ground-truth measured manually over 1300 nodules taken from more than 240 CT volumes, the performance of the proposed approach is evaluated in comparison with two other techniques: Local Density Maximum algorithm and the original EF. The results show that our approach provides the most accurate size estimates.