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2008
IEEE

Supervised methods for perfect segmentation in medical images

9 years 5 months ago
Supervised methods for perfect segmentation in medical images
We pose the problem of perfect segmentation for regions with ambiguous boundaries. We design machine learning classifiers to identify boundaries and build these into an interactive contouringframework. Experiments using synthetic and Multiple Sclerosis (MS) textures show the success of the classifiers. Experiments using the contouring tool reveal significant improvement in accuracy and inter/intra-operator variability over freehand delineation in synthetic images. We do not see the same improvement for MS lesions, which are small and their true boundaries undefined. The approach goes some way toward achieving perfect segmentation and extends naturally to other medical applications.
Tony Shepherd, Daniel C. Alexander
Added 20 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2008
Where ICIP
Authors Tony Shepherd, Daniel C. Alexander
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