A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.
A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.
复制标题
DOI：
10.1016/j.acra.2018.01.004
复制
发表时间：
2018-09
影响因子：
4.8
通讯作者：
Fan Y
中科院分区：
文献类型：
--
作者：
Zheng Q;Warner S;Tasian G;Fan Y
Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task due to high speckle noise, low contrast, and large appearance variations of kidneys in US images. Since texture features may improve the US image segmentation performance, we propose a novel graph-cuts method to segment kidney from US images by integrating image intensity information and texture feature maps. We develop a new graph cuts based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation. Experiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index=0.9446, average Mean Distance=2.2551, average Specificity=0.9971, average Accuracy=0.9919), better than other methods under comparison (p<0.05, paired Wilcoxon rank sum tests). The proposed method achieved promising performance for segmenting kidneys in 2D US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such progression chronic kidney disease.
登录
查看更多内容
超声图像中乳腺肿瘤的稳健的基于图的分割方法
影响因子：
4.2
作者：
Huang, Qing-Hua;Lee, Su-Ying;Li, An-Hua
通讯作者：
Li, An-Hua
DOI：
10.1109/tpami.2004.60
发表时间：
2004-09-01
影响因子：
23.6
作者：
Boykov, Y;Kolmogorov, V
通讯作者：
Kolmogorov, V
影响因子：
1.1
作者：
Le, T. H.;Jung, S-W.;Ko, S-J.
通讯作者：
Ko, S-J.
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
通讯作者：
DINSTEIN, I
DOI：
10.1002/jum.14209
发表时间：
2017-11
期刊：
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
影响因子：
--
作者：
Gao J;Perlman A;Kalache S;Berman N;Seshan S;Salvatore S;Smith L;Wehrli N;Waldron L;Kodali H;Chevalier J
通讯作者：
Chevalier J