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Automatic Detection and Segmentation of Kidneys in Magnetic Resonance Images Using Image Processing Techniques

机译:利用图像处理技术自动检测和分割磁共振图像中的肾脏

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Kidney Detection and Segmentation in MR images allows extracting meaningful information for nephrologists, also for practical use in clinical routine, thus we should apply an fast, automatic and robust algorithm. We demonstrate the possibility of construct an algorithm that achieve these requirements. Therefore, a novel kidney segmentation algorithm was created depending on multiple stages. The Region of Interest (ROI) is extracted after we convert the input image to binary one via specific thresholding level yields from K Mean Clustering algorithm. The resulted binary image contain both of kidneys as the biggest regions, so we can isolate them after we calculate the objects areas in labeled image. Finally we can use some morphological operation to remove small objects surrounding the kidney region. The effectiveness of this method is demonstrated through experimental results on complex MR slices. Kidneys were accurately detected and segmented in a few seconds.
机译:MR图像中的肾脏检测和分割可以为肾脏病医生提取有意义的信息,也可以在临床常规中实际使用,因此,我们应该应用快速,自动和鲁棒的算法。我们证明了构造满足这些要求的算法的可能性。因此,根据多个阶段创建了一种新颖的肾脏分割算法。在将输入图像通过K Mean Clustering算法通过特定的阈值级别输出将输入图像转换为二进制图像后,提取感兴趣区域(ROI)。生成的二进制图像包含两个肾脏作为最大区域,因此我们可以在计算标记图像中的对象区域后将其隔离。最后,我们可以使用一些形态学操作来去除肾脏区域周围的小物体。通过对复杂MR切片的实验结果证明了该方法的有效性。几秒钟内就可以准确地检测出肾脏并将其分割。

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