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Segmentation of liver in ultrasonic images applying local optimal threshold method

机译:基于局部最优阈值法的超声图像肝脏分割

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Low brightness contrast and grey level discontinuities of the ultrasonic liver image make it difficult to segment the object and the background and to extract the edges of the object using the global optimal threshold method. In this paper, we investigate a local optimal threshold method for the segmentation of ultrasound liver image. First of all, the distributed energy of the ultrasound liver image is estimated in the proposed liver segmentation. Then, the polynomials are fitted from the distributed energy data and a peak zone is defined from the minimum of the fitted polynomials. Finally, a few blocked images are divided from the number of the peak zones. Furthermore, multiple local optimal thresholds are obtained from the blocked images using Otsu's method, and the ultrasonic liver image is segmented according to all local optimal thresholds. Experimental results validate the segmentation and edge detection of liver in the ultrasound images.
机译:超声肝图像的低亮度对比度和灰度不连续性使得难以使用全局最佳阈值方法分割对象和背景以及提取对象的边缘。在本文中,我们研究了用于超声肝图像分割的局部最优阈值方法。首先,在提议的肝分割中估计超声肝图像的分布能量。然后,根据分布的能量数据拟合多项式,并根据拟合多项式的最小值定义峰区域。最终,从峰区域的数量中划分出一些被遮挡的图像。此外,使用Otsu方法从块图像中获得多个局部最优阈值,并根据所有局部最优阈值对超声肝图像进行分割。实验结果验证了超声图像中肝脏的分割和边缘检测。

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