一种新的超声图像分割区域生长方法

X. Hao, Charles J Bruce, C. Pislaru, James F. Greenleaf
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引用次数: 68

摘要

区域生长是在多特征向量空间中进行的。提出了控制区域生长的三个新的判据。首先,该方法不像传统的区域生长方法那样使用局部信息,而是使用全局信息。其次,为了克服散斑噪声和衰减伪影的影响,引入了“地理相似性”的新思想。第三,采用机会均等的能力标准,使结果与处理顺序无关。对体内心内超声图像的分割结果及相应的统计分析表明,该方法是可靠有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel region growing method for segmenting ultrasound images
Region growing is performed in a multi-feature vector space. Three novel criteria are developed for region growing control. First, instead of using the local information as do the conventional region growing methods, this method uses global information. Second, to overcome the effects of speckle noise and attenuation artifacts, a new idea termed, "geographic similarity", is introduced. Third, an equal opportunity competence criterion is employed to make results independent of processing order. Segmentation results for in vivo intracardiac ultrasound images and the corresponding statistical analyses show that this method is reliable and effective.
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