基于非线性增强和图割的CT序列肝脏肿瘤自动分割
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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.
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 on3 Dircadb 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.
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 on3 Dircadb 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.
引文
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[3]Rajagopal R,Subbaiah P.A survey on liver tumor detection and segmentation methods[J].ARPN Journal of Engineering and Applied Sciences,2015,10(6):2681-2685
[4]Foruzan A H,Chen Y W.Improved segmentation of low-contrast lesions using sigmoid edge model[J].International Journal of Computer Assisted Radiology and Surgery,2016,11(7):1267-1283
[5]Wu W W,Wu S C,Zhou Z H,et al.3D liver tumor segmentation in CT images using improved fuzzy C-means and graph cuts[J].BioMed Research International,2017,2017:Article No.5207685
[6]H?me Y,Pollari M.Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation[J].Medical Image Analysis,2012,16(1):140-149
[7]Li B N,Chui C K,Chang S,et al.A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images[J].Expert Systems with Applications,2012,39(10):9661-9668
[8]Kadoury S,Vorontsov E,Tang A.Metastatic liver tumour segmentation from discriminant Grassmannian manifolds[J].Physics in Medicine and Biology,2015,60(16):6459-6478
[9]Huang W,Li N,Lin Z,et al.Liver tumor detection and segmentation using kernel-based extreme learning machine[C]//Proceedings of the 35th Annual International Conference of IEEE Engineering in Medicine and Biology Society.Los Alamitos:IEEE Computer Society Press,2013:3662-3665
[10]Moghbel M,Mashohor S,Mahmud R,et al.Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring[J].EXCLI Journal,2016,15:406-423
[11]Shi C F,Cheng Y Z,Liu F,et al.A hierarchical local region-based sparse shape composition for liver segmentation in CT images[J].Pattern Recognition,2016,50:88-106
[12]Peng J L,Wang Y,Kong D X.Liver segmentation with constrained convex variational model[J].Pattern Recognition Letters,2014,43:81-88
[13]Lu X Q,Wu J S,Ren X Y,et al.The study and application of the improved region growing algorithm for liver segmentation[J].Optik,2014,125(9):2142-2147
[14]Liao M,Zhao Y Q,Liu X Y,et al.Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching[J].Computer Methods and Programs in Biomedicine,2017,143:1-12
[15]Boykov Y,Kolmogorov V.An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9):1124-1137
[16]Soler L,Hostettler A,Agnus V,et al.3D image reconstruction for comparison of algorithm database:a patient-specific anatomical and medical image database[OL].[2018-05-08].https://www.ircad.fr/research/3dircadb/