A variational segmentation framework using active contours and thresholding

Authors:
Samuel Dambreville;Marc Niethammer;Anthony Yezzi;Allen Tannenbaum
Affiliations:
Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology
Venue:
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Year:
2007

Citing 10
Cited 0

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Abstract

Segmentation involves separating distinct regions in an image. In this note, we present a novel variational approach to perform this task. We propose an energy functional that naturally combines two segmentation techniques usually applied separately: intensity thresholding and geometric active contours. Although our method can deal with more complex image statistics, intensity averages are used to separate regions, in this present work. The proposed approach affords interesting properties that can lead to sensible segmentation results.