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An Efficient Modified Level Set Method For Brain Tissue Segmentation

机译:一种高效的脑组织分割级别仪表方法

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The paper presents a new efficient method for brain tissue extraction. Firstly, the speed of segmentation is enhanced through improving classical distance matrix. It can accelerate the distance function convergence faster, and the accuracy is not reduced simultaneously. Secondly, the uniqueness of classical result is changed through the improved method. The evolving lines will be stopped at the same level gray, so the primal fluid can be wiped off. White matter and gray matter are extracted more accurate. Finally, a dynamic condition for ending iteration is presented through comparing the interval frames. The improvement changes the flaw of setting evolving times to end iteration, so it can make the veracity and speed much better. The methods are generally applied to image 2D and 3D segmentation, and the results of experiment indicate that the improvements can make the brain tissue extraction more rapid and accurate, and will be very helpful for doctor to make a definite diagnosis.
机译:本文提出了一种新的脑组织提取方法。首先,通过改善经典距离矩阵来提高分割速度。它可以更快地加速距离功能会聚,并且精度不会同时减少。其次,通过改进的方法改变了经典结果的唯一性。进化的线条将在相同的灰度下停止,因此可以擦除原始流体。白质和灰质萃取更准确。最后,通过比较间隔帧来呈现用于结束迭代的动态条件。改进改变了设置不断变化的漏洞,以结束迭代,因此它可以更好地使得速度和速度更好。这些方法通常应用于图像2D和3D分割,实验结果表明,改进可以使脑组织提取更快速和准确,并且对医生产生明确的诊断非常有帮助。

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