title: Spatial anatomic knowledge for 2-D interactive medical image segmentation and matching.
abstract: A representation is described for two-dimensional anatomic shapes which can be described by single-valued distortions of a circle. The representation, called a radial contour model, is both generic, in that it captures the expected shape as well as the range of variation for an anatomic shape class, and flexible, in that the model can deform to fit an individual instance of the shape class. The model is implemented in a program called SCANNER (version 0.61) for 2-D interactive image segmentation and matching. An initial evaluation was performed using 7 shape models learned from a training set of 93 contours, and a control model containing no shape knowledge. Evaluation using 60 additional contours showed that in general the shape knowledge should reduce interactive segmentation time by a factor of two over the control, and that for specific shapes such as the eye, the improvement is much greater. A matching function was also devised which showed that the radial contour model should allow diagnosis of subtle shape changes. These results suggest that the use of spatial anatomic knowledge, when combined with good interactive tools, can help to alleviate the segmentation bottleneck in medical imaging. The models, when extended to more complex shapes, will form the spatial component of a knowledge base of anatomy that could have many uses in addition to image segmentation.
authors.affiliation: Department of Biological Structure, University of Washington, Seattle 98195.
authors.forename: J F
authors.initials: JF
authors.lastname: Brinkley
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated; D012984:Software