Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Richards Building, 7th floor, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116; School of Computer and Control Engineering, Yantai University, Yantai, China.
Division of Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Acad Radiol. 2018 Sep;25(9):1136-1145. doi: 10.1016/j.acra.2018.01.004. Epub 2018 Feb 12.
Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in 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 < .05, paired Wilcoxon rank sum tests).
The proposed method achieved promising performance for segmenting kidneys in two-dimensional 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 as progression of chronic kidney disease.
由于超声（US）图像中的高斑点噪声、低对比度和肾脏外观变化大，因此自动分割肾脏仍然是一项具有挑战性的任务。由于纹理特征可以提高 US 图像分割性能，我们提出了一种新的基于图割的方法，通过整合图像强度信息和纹理特征图来分割 US 图像中的肾脏。
我们开发了一种新的基于图割的方法，通过整合原始图像强度信息和使用 Gabor 滤波器提取的纹理特征图来分割肾脏 US 图像。为了处理肾脏图像中较大的外观变化并提高计算效率，我们构建了一个靠近肾脏边界的图像像素图，而不是构建整个图像的图。为了使肾脏分割对弱边界具有鲁棒性，我们采用局部区域信息来测量图像像素之间的相似性，以计算边缘权重来构建图像像素图。局部图是动态更新的，基于图割的分割迭代进行，直到收敛。我们的方法已经基于 85 个对象的肾脏 US 图像进行了评估。使用 20 个随机选择的对象的成像数据作为训练数据来调整图像分割方法的参数，并用剩余的数据作为测试数据进行验证。
实验结果表明，该方法对双侧肾脏的分割效果良好（平均 Dice 指数=0.9446，平均平均距离=2.2551，平均特异性=0.9971，平均准确性=0.9919），优于其他比较方法（P<.05，配对 Wilcoxon 秩和检验）。
该方法在二维 US 图像中分割肾脏的性能良好，优于基于图像信息单一通道的分割方法。该方法将有助于提取可能预测慢性肾脏病进展等重要临床结果的肾脏特征。
