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A 3 - D Liver Tumor Level Set Method Based on Narrowband Gradient Modification

机译:一种基于窄带梯度修改的3 - D肝肿瘤水平集法

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Liver tumor segmentation is a hot issue in current medical image processing research, fast and accurate liver tumor segmentation method for abdominal CT sequences is the basis for liver lesions diagnose. The evolving curve in traditional level set algorithm often stopped in local gradient minimal regions or false edges while dealing with low contrast or borderline blurred CT images. In order to solve this problem, a level set method based on narrowband gradient modification is proposed in this paper. First, morphological gradient transformation was performed on the image to get the gradient image. Then it set up function relationship between structural elements radius and gradient value, and modified the image gradient. Using level set method to segment the image according to gradient information. Finally using the result as the initial contour of the adjacent CT sequence, built narrowband with a special width, split the liver tumor within the narrowband, repeat the process until getting all the results of the entire slices, then 3-D reconstructed.
机译:肝脏肿瘤分割是当前医学图像处理研究中的一个热门问题,腹部CT序列的快速和准确的肝肿瘤分割方法是肝病变诊断的基础。传统级别集算法中的不断变化的曲线通常在局部梯度最小区域或假边缘中停止,同时处理低对比度或边界模糊的CT图像。为了解决这个问题,本文提出了一种基于窄带梯度修改的水平集方法。首先,对图像进行形态梯度变换以获得梯度图像。然后它在结构元素半径和梯度值之间设置功能关系,并修改了图像梯度。使用级别设置方法根据梯度信息对图像进行分割。最后使用结果作为相邻CT序列的初始轮廓,构建窄带,具有特殊宽度,在窄带内分离肝肿瘤,重复该过程直到得到整个切片的所有结果,然后重建3-D。

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