Qiang Zheng1, Steven Warner2, Gregory Tasian2, Yong Fan3. 1. 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. 2. Division of Urology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania. 3. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Richards Building, 7th floor, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116. Electronic address: yong.fan@ieee.org.
Abstract
RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
Authors: Shi Yin; Qinmu Peng; Hongming Li; Zhengqiang Zhang; Xinge You; Katherine Fischer; Susan L Furth; Gregory E Tasian; Yong Fan Journal: Med Image Anal Date: 2019-11-08 Impact factor: 8.545
Authors: Shi Yin; Zhengqiang Zhang; Hongming Li; Qinmu Peng; Xinge You; Susan L Furth; Gregory E Tasian; Yong Fan Journal: Proc IEEE Int Symp Biomed Imaging Date: 2019-07-11
Authors: Jaidip M Jagtap; Adriana V Gregory; Heather L Homes; Darryl E Wright; Marie E Edwards; Zeynettin Akkus; Bradley J Erickson; Timothy L Kline Journal: Abdom Radiol (NY) Date: 2022-04-27