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Proceedings Paper

New ultrasound image-segmentation algorithm based on an early vision model and discrete snake model
Author(s): Chung-Ming Chen; Henry Horng-Shing Lu; Yu-Chen Lin
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Paper Abstract

Segmentation is a fundamental step in many quantitative analysis tasks for clinical ultrasound images. However, due to the speckle noises and the ill-defined edges of the object of interest, the classic image segmentation techniques are frequently ineffective in segmenting ultrasound images. It is either difficult to identify the actual edges or the derived boundaries are disconnected in the images. In this paper, we present a novel algorithm for segmentation of general ultrasound images, which is composed of two major techniques, namely the early vision model and the discrete snake model. By simulating human early vision, the early vision model can highlight the edges and, at the same time, suppress the speckle noises in an ultrasound image. The discrete snake model carries out energy minimization on the distance map rather than performing snake deformation on the original image as other snake models did. Moreover, instead of searching the next position for a snaxel along its searching path pixel by pixel, the discrete model only consider the local maxima as the searching space. The new segmentation algorithm has been verified on clinical ultrasound images and the derived boundaries of the object of interest are quite consistent with those specified by medical doctors.

Paper Details

Date Published: 24 June 1998
PDF: 12 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310974
Show Author Affiliations
Chung-Ming Chen, National Taiwan Univ. College of Medicine (Taiwan)
Henry Horng-Shing Lu, National Chiao-Tung Univ. (Taiwan)
Yu-Chen Lin, National Taiwan Univ. College of Medicine and National Chiao-Tung Univ. (Taiwan)


Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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