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Efficient incomplete ellipse detection based on minor axis for ultrasound fetal head approximation

机译:基于短轴的高效不完全椭圆检测,用于超声胎儿头部逼近

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Fetal head detection and approximation from ultrasound image is an important method in obstetric and gynaecology. In this research, we propose a modification of an efficient algorithm to detect an ellipse shape in an image. Our proposed method is using an efficient way to approximate an ellipse based on its minor axis. By enumerating every possible minor axis from a pairs of pixels, other ellipse parameter can be estimated. For verifying the ellipse approximation result, a voting mechanism is conducted to vote the most appropriate set of parameters for an ellipse. Instead of using every edge pixels in the image, we randomize the pixels to gain speed improvement. We test the algorithm using two different data. The first one is real ultrasound image and the second one is synthetic image which has been populated with salt noise. The ultrasound image is cleaned from speckle noise using Speckle Reducing Anisotropic Diffusion (SRAD) algorithm. The experiment gives satisfying result in both of synthetic and real images.
机译:超声图像对胎儿头部的检测和逼近是妇产科的重要方法。在这项研究中,我们提出了一种有效算法的改进,可以检测图像中的椭圆形。我们提出的方法使用一种有效的方法来根据椭圆的短轴近似椭圆。通过从一对像素中枚举每个可能的短轴,可以估计其他椭圆参数。为了验证椭圆近似结果,执行投票机制以投票最合适的椭圆参数集。代替使用图像中的每个边缘像素,我们将像素随机化以提高速度。我们使用两个不同的数据测试算法。第一个是真实的超声图像,第二个是已填充盐噪声的合成图像。使用减少斑点各向异性扩散(SRAD)算法从斑点噪声中清除超声图像。实验在合成图像和真实图像上均给出令人满意的结果。

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