標題: An adaptive snake model for ultrasound image segmentation: Modified trimmed mean filter, ramp integration and adaptive weighting parameters
作者: Chen, CM
Lu, HHS
統計學研究所
Institute of Statistics
關鍵字: adaptive weighting parameters;modified trimmed mean filter;ramp integration;snake model;ultrasound image segmentation
公開日期: 1-Oct-2000
摘要: The snake model is a widely-used approach to finding the boundary of the object of interest in an ultrasound image. However, due to the speckles, the weak edges and the tissue-related textures in an ultrasound image, conventional snake models usually cannot obtain the desired boundary satisfactorily. In this paper, we propose a new adaptive snake model for ultrasound image segmentation. The proposed snake model is composed of three major techniques, namely, the modified trimmed mean (MTM) filtering, ramp integration and adaptive weighting parameters. With the advantages of the mean and median filters, the MIM filter is employed to alleviate the speckle interference in the segmentation process. The weak edge enhancement by ramp integration attempts to capture the slowly varying edges, which are hard to capture by conventional snake models. The adaptive weighting parameter allows weighting of each energy term to change adaptively during the deformation process. The proposed snake model has been verified on the phantom and clinical ultrasound images. The experimental results showed that the proposed snake model achieves a reasonable performance with an initial contour placed 10 to 20 pixels away from the desired boundary. The mean minimal distances from the derived boundary to the desired boundary have been shown to be less than 3.5 (for CNR greater than or equal to 0.5) and 2.5 pixels, respectively, for the phantom and ultrasound images.
URI: http://hdl.handle.net/11536/30223
ISSN: 0161-7346
期刊: ULTRASONIC IMAGING
Volume: 22
Issue: 4
起始頁: 214
結束頁: 236
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