Supervised Variational Model With Statistical Inference and Its Application in Medical Image Segmentation

Supervised Variational Model With Statistical
Inference and Its Application in Medical
Image Segmentation

Abstract— Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level set algorithms often assume piecewise constant or piecewise smooth for segments, which are implausible for general medical image segmentation. Furthermore,low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms.Thus, to address these problems, < Final Year Projects 2016 > we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region < den-sity > distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and back-ground.