Improved active contour segmentation model based on phase consistency
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摘要: 为了实现甲状腺超声图像中结节组织的快速准确分割,克服图像灰度分布不均匀和边缘模糊对分割结果造成的影响,采用了基于相位一致性改进的活动轮廓分割模型。首先,利用相位一致性边缘检测原理构造一种新的速度函数,不仅弥补了梯度算子边缘检测中由于滤波处理造成边缘损坏的缺陷,而且可以灵活地控制曲线演化速率;然后,将该速度函数乘入到无边缘主动轮廓模型的能量项中,避免了线性组合中的权重分配问题,同时具有全局分割能力。通过理论分析和实验验证,改进模型的相对差异度均小于1%,运行时间均低于对比模型。结果表明,新模型实现了灰度分布不均匀图像的精确分割,同时分割效率也有所提高。Abstract: In order to realize rapid and accurate segmentation of nodular tissue in thyroid ultrasound images and overcome the effects of uneven gray distribution and fuzzy boundary of images on segmentation results, an improved active contour segmentation model based on phase congruency was proposed. First, a new speed function was constructed by means of the principle of phase congruency edge detection. It not only made up the defect of edge damage due to filter processing during gradient operator edge detection but also controlled curve evolution rate flexibly. After that, the speed function was introduced to the energy term of active contour model without edge to avoid the problem of weight distribution in the linear combination and have global segmentation ability. Through theoretical analysis and experimental verification,the relative difference of the improved model is less than 1% and the running time is less than that of the comparison models. The results show that the new model achieves accurate segmentation of image with uneven gray distribution and the efficiency of segmentation is improved.
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