Automatic Segmentation of Liver Tumor in CT Volumes Using Nonlinear Enhancement and Graph Cuts
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摘要: 针对腹部CT图像肝脏肿瘤对比度低、边界模糊、灰度多样等因素引起的分割困难,提出基于非线性增强和图割的肝脏肿瘤自动分割.首先根据肝脏区域灰度分布特性,采用自适应分段非线性增强和迭代卷积操作提高正常肝实质与肿瘤组织的对比度;然后将增强结果和图像边界信息有效地融入图割能量函数,实现肝脏肿瘤初步自动分割结果;最后采用三维形态学开操作对初步分割结果进行优化,去除其中的误分割区域,提高分割精度.在3Dircadb和XYH数据库上的实验结果表明,该方法能有效地自动分割腹部CT序列中的肝脏肿瘤,且综合分割性能优于现有多种方法.Abstract: Aiming at the segmentation challenges caused by low contrast, fuzzy boundary and variant grayscale of liver tumors in abdominal CT images, an automatic liver tumor segmentation method based on nonlinear enhancement and graph cuts is proposed. Firstly, adaptive piecewise nonlinear enhancement and iterative convolution operation are used to improve the contrast of healthy liver parenchyma and tumors according to the gray-level distribution characteristics of liver region. Then, the enhancement result and image edge information are effectively integrated into graph cuts cost computation to segment the liver tumors initially and automatically. Finally, three-dimensional morphological opening operation is performed on the initial segmentation result to remove segmentation errors and increase accuracy. The experimental results on 3Dircadb and XYH databases show that the proposed method can segment liver tumors from abdominal CT volumes effectively and automatically, and the comprehensive segmentation performance of the proposed method is superior to that of several existing methods.
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Keywords:
- medical image segmentation /
- graph cuts /
- nonlinear enhancement /
- liver tumor
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