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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >A region and gradient based active contour model and its application in boundary tracking on anal canal ultrasound images
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A region and gradient based active contour model and its application in boundary tracking on anal canal ultrasound images

机译:基于区域和梯度的主动轮廓模型及其在肛管超声图像边界跟踪中的应用

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摘要

A novel Gaussian mixture model (GMM)-based region and gradient active contour model is proposed for general object boundary tracking and the purpose of boundary tracking for anal muscle layers. Motion information is extracted from adjacent slice and is used to guide the first step of boundary tracking procedure. The idea is that GMM is introduced into the statistical feature computation for object region and background region, thereby providing an accurate model for regional pixel intensity description. Expectation-maximization algorithm and K-means algorithm are used for parameter solutions of GMM. Based on the available region information, gradient information and the self-constraints of the contour, a unifying active contour model is proposed. The proposed active contour models and tracking algorithm were tested on synthetic images and simulated ultrasound images to evaluate some generic features of the model for boundary tracking. Furthermore, it was applied to perform boundary tracking of anal muscle layers. The tracking results were evaluated by three experts. The results showed that the proposed method has a good performance for boundary tracking on anal wall ultrasound image. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:提出了一种基于高斯混合模型(GMM)的区域和梯度有效轮廓模型,用于一般物体边界跟踪和肛门肌肉层边界跟踪。从相邻切片中提取运动信息,并将其用于指导边界跟踪过程的第一步。想法是将GMM引入对象区域和背景区域的统计特征计算中,从而为区域像素强度描述提供准确的模型。期望最大化算法和K-means算法用于GMM的参数解。基于可用的区域信息,梯度信息和轮廓的自约束,提出了一种统一的主动轮廓模型。在合成图像和模拟超声图像上测试了提出的主动轮廓模型和跟踪算法,以评估模型的一些通用特征以进行边界跟踪。此外,它被用于执行肛门肌肉层的边界跟踪。跟踪结果由三位专家评估。结果表明,该方法对肛门壁超声图像的边界跟踪具有良好的性能。 (c)2007模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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