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Particle swarm optimation based 2-dimensional randomized hough transform for fetal head biometry detection and approximation in ultrasound imaging

机译:基于粒子群优化的二维随机霍夫变换用于超声成像中胎儿头部生物特征的检测和近似

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One of the most profound use of ultrasound imaging is to generate the image of fetal during pregnancy. This paper will describe an ellipse detection approach to automatically detect and approximate the head size of the fetal. The method was developed using the Hough Transform techniques that have been modified and optimized by Particle Swarm Optimization (PSO). Experiments of the method are tested on synthetic and real ellipse image dataset. For real images, the detection was applied on 2D ultrasonography images to perform fetal head measurement to approximate the Head Circumference (HC) and Biparietal Diameter (BPD). Experiment result showed that the proposed method can perform ellipse detection in synthetic dataset with satisfactory result for noisy images with noise density up to 0.4 and able to perform the fetal head detection for real images with an averate hit rate of 0.654. This proposed method can also perform detection on images that have high degree of noise or incomplete ellipse images generated from the fetal objects.
机译:超声成像最深刻的用途之一是在怀孕期间生成胎儿图像。本文将介绍一种椭圆形检测方法,可自动检测并近似胎儿的头部大小。该方法是使用霍夫变换技术开发的,该技术已通过粒子群优化(PSO)进行了修改和优化。在合成和真实椭圆图像数据集上测试了该方法的实验。对于真实图像,将检测应用于2D超声图像以执行胎儿头部测量,以近似头部围(HC)和双顶径(BPD)。实验结果表明,该方法可以在合成数据集中进行椭圆检测,对于噪声密度高达0.4的高噪声图像,效果令人满意;对于真实图像,平均命中率为0.654,可以进行胎儿头部检测。该提出的方法还可以对具有高噪声度的图像或从胎儿物体生成的不完整的椭圆形图像执行检测。

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