A fast algorithm for global minimization of maximum likelihood based on ultrasound image segmentation

Jie Huang; Xiaoping Yang; Yunmei Chen

doi:10.3934/ipi.2011.5.645

Article Contents

2011, Volume 5, Issue 3: 645-657. Doi: 10.3934/ipi.2011.5.645

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A fast algorithm for global minimization of maximum likelihood based on ultrasound image segmentation

1.
Department of Applied Mathematics, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094
2.
Department of Mathematics, University of Florida, Gainesville, FL 32611

Received: November 2010

Revised: April 2011

Published: August 2011

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Abstract

This paper presents a novel variational model for ultrasound image segmentation that uses a maximum likelihood estimator based on Fisher-Tippett distribution of the intensities of ultrasound images. A convex relaxation method is applied to get a convex model of the subproblem with fixed distribution parameters. The relaxed subproblem, which is convex, can be fast solved by using a primal-dual hybrid gradient algorithm. The experimental results on simulated and real ultrasound images indicate the effectiveness of the method presented.

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Mathematics Subject Classification: Primary: 62H35; Secondary: 65K10.

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