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Active contour model driven by global and local intensity information for ultrasound image segmentation

机译:由全局和局部强度信息驱动的主动轮廓模型,用于超声图像分割

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摘要

Due to the abundant noise, blurry boundaries, and intensity inhomogeneities present in ultrasound (US) images, it is a difficult task to segment US images accurately. In this paper, we propose a novel active contour method that combines global and local region information to achieve this task. The global information can segment US images with noise and blurry boundaries; while the local information can settle the intensity homogeneities of images. The proposed method can be directly applied to synthetic, real, and US-images segmentation. Results demonstrate the superiority of the proposed method over other repreSentative algorithms. Moreover, we also extend the proposed method to vectorvalued images. Experiments are performed to testify the feasibility of the method, and the proposed vector-valued idea can be applied to the medical co-segmentation in the future. (C) 2018 Elsevier Ltd. All rights reserved.
机译:由于超声(US)图像中存在大量噪声,边界模糊和强度不均匀,因此准确分割US图像是一项艰巨的任务。在本文中,我们提出了一种新颖的主动轮廓方法,该方法结合了全局和局部区域信息来完成此任务。全球信息可以分割带有噪点和模糊边界的美国图像;而本地信息可以解决图像的强度均匀性。所提出的方法可以直接应用于合成,真实和美国图像分割。结果证明了该方法优于其他代表算法的优越性。此外,我们还将所提出的方法扩展到矢量值图像。通过实验验证了该方法的可行性,提出的向量值思想可以在未来的医学领域中得到应用。 (C)2018 Elsevier Ltd.保留所有权利。

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