Integrating Intensity and Boundary Information for Tissue Classification

Authors:
Dzung L. Pham
Affiliations:
Johns Hopkins University, Baltimore, MD
Venue:
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Year:
2004

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Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty

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Abstract

A new algorithm is proposed for performing unsupervised tissue classification in medical images by integrating conventional clustering techniques with edge-adaptive segmentation techniques. Based on the fuzzy C-means algorithm, the algorithm computes a smooth segmentation while simultaneously estimating an edge field. Unlike most tissue classification algorithms that incorporate a smoothness constraint, the edge field estimation prevents the algorithm from smoothing across tissue boundaries, thereby producing robust yet accurate results. The algorithm is formulated as the minimization of an objective function that includes penalty terms to ensure that both the segmentation and edge field are relatively smooth.