Adaptive Segmentation of Ultrasound Images

Barbara Levienaise-Obadia and Andrew Gee
University of Cambridge
Department of Engineering
Cambridge CB2 1PZ
{ 92bvl,ahg } @eng.cam.ac.uk

Abstract:

This paper describes a novel approach to the semi-automatic segmentation of ultrasound images. Assisted segmentation is particularly attractive when processing many slices through a 3D data set, and even though fully automatic segmentation would be ideal, this is currently not feasible given the quality of ultrasound images. The algorithm developed in this paper is based on the active contour paradigm, with several important modifications. The contour is attracted to boundaries described locally by statistical models: this allows for the fact that the definition of what constitutes a boundary may vary around the boundary's length. The statistical models are trained on-the-fly by observing boundaries accepted by the operator. In this way, operator intervention in a particular slice is sensibly exploited to reduce the need for intervention in subsequent slices. The resulting algorithm provides fast, reliable and verifyable segmentation of in-vivo ultrasound images.

• 1 Introduction
• 2 B-spline and piecewise linear snakes
• 3 Properties of boundaries
• 4 On-the-fly training
• 5 Results
• 6 Conclusions
• References