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Unsupervised Multiphase Segmentation: A Phase Balancing Model
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DOI：10.1109/TIP.2009.2032310.png)
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Abstract
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Variational models have been studied for image segmentation application since the Mumford-Shah functional was introduced in the late 1980s. In this paper, we focus on multiphase segmentation with a new regularization term that yields an unsupervised segmentation model. We propose a functional that automatically chooses a favorable number of phases as it segments the image. The primary driving force of the segmentation is the intensity fitting term while a phase scale measure complements the regularization term. We propose a fast, yet simple, brute-force numerical algorithm and present experimental results showing the robustness and stability of the proposed model.
Keywords:
Cheeger Set
image segmentation
multiphase
scale
variational model
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