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Ultrasound image segmentation with multilevel threshold based on differential search algorithm

机译:基于差分搜索算法的多阈值超声图像分割

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Ultrasound (US) image segmentation plays a very important role in diagnostic imaging. In order to extract special tissues from images, this study proposes a new method for segmentation of US images. The proposed method uses multilevel threshold segmentation which is based on Otsu and differential search algorithm. Testing in simulation US images shows that the proposed algorithm has a better result of segmentation than the three existing methods, including region growing, the active contour model and k-means technique. The proposed method gets the highest F-m values and the smallest area errors in experiments. Vivo US images are also tested by the proposed method and it achieves a good segmentation result.
机译:超声(US)图像分割在诊断成像中起着非常重要的作用。为了从图像中提取特殊组织,本研究提出了一种新的分割美国图像的方法。所提出的方法使用基于Otsu和差分搜索算法的多级阈值分割。在美国模拟仿真图像中的测试表明,与区域扩展,主动轮廓模型和k均值技术这三种现有方法相比,该算法具有更好的分割效果。该方法在实验中获得了最高的F-m值和最小的面积误差。 Vivo US图像也通过提出的方法进行了测试,并获得了良好的分割效果。

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