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Medical ultrasound image segmentation using genetic active contour

机译:基于遗传主动轮廓的医学超声图像分割

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Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation; however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images.
机译:图像分割是图像处理的最早也是最重要的阶段之一,并且在医学超声图像的定性和定量分析中都起着重要作用,但是超声图像的对比度较低,并且会被强烈的斑点噪声破坏。由于这些效应,超声图像的分割非常具有挑战性,传统的图像分割方法可能无法获得令人满意的结果。主动轮廓法已成为广泛应用的图像分割技术之一。然而,由于超声图像质量低下,因此遇到了困难。在本文中,我们提出了一种结合遗传算法和主动轮廓的分段方法,并基于遗传算法实现了能量最小化过程。已经提出了该方法来克服经典活动轮廓的一些限制,例如轮廓初始化和局部最小值(斑点噪声),并且已经成功地应用于医学超声图像。在医学超声图像上的实验结果表明,我们提出的方法只能正确分割超声图像上的圆形组织。

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