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Using prior shape and intensity profile in medical image segmentation

机译:在医学图像分割中使用先前的形状和强度分布

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In this note we present a coupled optimization model for boundary determination. One part of the model incorporates a prior shape into a geometric active contour model with a fixed parameter. The second part determines the 'best' parameter used in the first part by maximizing the mutual information of the image geometry between the prior and an aligned novel image over all the alignments that are the solutions of the first part corresponding to different parameters. We also present an alternative method, which generates an intensity model formed as the average of a set of aligned training images. Experimental results on cardiac ultrasound images are presented. These results indicate that the proposed model provides close agreement with expert traced borders, and the parameter determined in this model for one image can be used for images with similar properties. The existence of a solution to the proposed minimization problem is also discussed.
机译:在本说明中,我们介绍了一个用于边界确定的耦合优化模型。该模型的一部分将先前的形状包含到具有固定参数的几何活动轮廓模型中。第二部分通过在所有对准的所有对准中最大化先前和对齐的新颖图像之间的图像几何形状的相互信息来确定第一部分中使用的“最佳”参数。我们还提出了一种替代方法,该方法产生形成为一组对准训练图像的平均值的强度模型。提出了心脏超声图像的实验结果。这些结果表明,该模型与专家跟踪边界提供了密切的协议,并且在该模型中确定一个图像中确定的参数可用于具有相似属性的图像。还讨论了所提出的最小化问题的解决方案。

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