Watershed segmentation using prior shape and appearance knowledge

Authors:
Ghassan Hamarneh;Xiaoxing Li
Affiliations:
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6;School of Computing Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6
Venue:
Image and Vision Computing
Year:
2009

Citing 10
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Abstract

Watershed transformation is a common technique for image segmentation. However, its use for automatic medical image segmentation has been limited particularly due to oversegmentation and sensitivity to noise. Employing prior shape knowledge has demonstrated robust improvements to medical image segmentation algorithms. We propose a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge. Our method iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments. Quantitative validation of magnetic resonance imaging segmentation results supports the robust nature of our method.