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Segmentation of Brain MRI Image with GVF Snake Model

机译:GVF Snake模型分割脑部MRI图像

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摘要

Medical image segmentation is the foundation and research focus in the medical image processing field. In this paper the normalized GVF Snake model combines with traditional edge detection is proposed for the brain MRI image semiautomatic segmentation. The thinning Canny result is used to calculate the edge map gradient of the GVF snake model. Then the normalized GVF snake model deforms with the manual initial contour. Simulation results show that this method can extract the boundary of the tumor accurately, and the method can overcome the problem that traditional GVF snake cannot efficient converge to the weak boundary. The method has positive significance in practical applications.
机译:医学图像分割是医学图像处理领域的基础和研究重点。本文提出了归一化的GVF Snake模型与传统的边缘检测相结合的方法,用于脑MRI图像半自动分割。稀化Canny结果用于计算GVF蛇模型的边缘贴图梯度。然后,归一化的GVF蛇模型随着手动初始轮廓变形。仿真结果表明,该方法能够准确提取出肿瘤的边界,克服了传统GVF蛇不能有效收敛到弱边界的问题。该方法在实际应用中具有积极意义。

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