首页> 外文会议>The 1st International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007) >Adaptive Level Set Method for Segmentation of Liver Tumors in Minimally Invasive Surgery Using Ultrasound Images
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Adaptive Level Set Method for Segmentation of Liver Tumors in Minimally Invasive Surgery Using Ultrasound Images

机译:超声图像在微创手术中肝肿瘤分割的自适应水平集方法

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Ultrasound images have been employed in guiding clinical interventional therapy procedures for liver tumor.However, segmenting liver tumor in the ultrasound images presents a unique challenge because of the low-contrast objects in the noisy image. Snakes, or active contours have had limited success in such noisy and complex image. In this paper, an adaptive level set method is proposed, which combines the global statistics and boundary statistics instead of image gradient and edge strength .Compared to traditional level set method, the experiment results show that the proposed level set method was feasible, enabled accurate and robust.
机译:超声图像已被用于指导肝肿瘤的临床介入治疗程序。但是,由于噪声图像中的对比度较低,因此在超声图像中分割肝肿瘤提出了独特的挑战。蛇或活动轮廓在这种嘈杂而复杂的图像中获得的成功有限。本文提出了一种自适应的水平集方法,该方法结合了全局统计量和边界统计量,而不是图像的梯度和边缘强度。与传统的水平集方法相比,实验结果表明,该水平集方法可行,准确。坚固。

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