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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >A disk expansion segmentation method for ultrasonic breast lesions
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A disk expansion segmentation method for ultrasonic breast lesions

机译:超声乳腺病变的椎间盘扩张分割方法

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摘要

Automatically extracting lesion boundaries in ultrasound images is difficult due to the variance in shape and interference from speckle noise. An effective scheme of removing speckle noise can facilitate the segmentation of ultrasonic breast lesions, which can be performed with an iterative disk expansion method. In this study, a disk expansion segmentation method is proposed to semi-automatically find lesion contours in ultrasonic breast image. To evaluate the performance of the proposed method, the simulations with seven types of cysts, three in vitro phantom images and 10 clinical breast images are introduced. The mean normalized true positive area overlap between simulated contours and contours obtained by the proposed method is over 85% in simulation results. A strong correlation exists between physicians' manual delineations and detected contours in clinical breast images. In addition, the method is also verified to be able to simultaneously contour multiple lesions in a single image. In comparison with the conventional active contour model, our proposed method does not require any initial seed within a lesion and thus, it is more convenient and applicable.
机译:由于形状的变化和斑点噪声的干扰,很难自动提取超声图像中的病变边界。去除斑点噪声的有效方案可以促进超声乳腺病变的分割,这可以通过迭代盘扩展方法来执行。在这项研究中,提出了一种磁盘扩展分割方法以半自动找到超声乳腺图像中的病变轮廓。为了评估该方法的性能,介绍了七种类型的囊肿,三张体外体模图像和十张临床乳腺图像的仿真结果。在模拟轮廓和通过该方法获得的轮廓之间的平均归一化真实正面积重叠在仿真结果中超过了85%。医师的手动描述与临床乳腺图像中检测到的轮廓之间存在很强的相关性。另外,该方法也被验证为能够在单个图像中同时轮廓化多个病变。与传统的主动轮廓模型相比,我们提出的方法在病变内不需要任何初始种子,因此更加方便和适用。

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