Edge completion from sparse data: a level-set approach

Zhujiang Cao; Benoit M. Dawant

doi:10.1117/12.533303

12 May 2004 Edge completion from sparse data: a level-set approach

Zhujiang Cao, Benoit M. Dawant

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.533303
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Intensity differences between objects are often used for segmentation. In an ideal situation, these differences permit the computation of edges that form complete contours around objects in the image. However, edges found in real images are usually a set of real and spurious disconnected boundary segments. Even more challenging are those so called apparent or subjective contours whose boundary are not defined by intensity or texture variations. In this paper, we present a novel method to segment and reconstruct images with missing boundaries, including images with large missing edges commonly found in ultrasound imaging. We test our algorithm on classic synthetic images, phantom images and on real ultrasound images of the bladder, heart, and colon.

Citation Download Citation

Zhujiang Cao and Benoit M. Dawant "Edge completion from sparse data: a level-set approach", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.533303

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