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Purpose: To improve kidney segmentation in clinical ultrasound (US) images,we develop a new graph cuts based method to segment kidney US images byintegrating original image intensity information and texture feature mapsextracted using Gabor filters. Methods: To handle large appearance variationwithin kidney images and improve computational efficiency, we build a graph ofimage pixels close to kidney boundary instead of building a graph of the wholeimage. To make the kidney segmentation robust to weak boundaries, we adoptlocalized regional information to measure similarity between image pixels forcomputing edge weights to build the graph of image pixels. The localized graphis dynamically updated and the GC based segmentation iteratively progressesuntil convergence. The proposed method has been evaluated and compared withstate of the art image segmentation methods based on clinical kidney US imagesof 85 subjects. We randomly selected US images of 20 subjects as training datafor tuning the parameters, and validated the methods based on US images of theremaining 65 subjects. The segmentation results have been quantitativelyanalyzed using 3 metrics, including Dice Index, Jaccard Index, and MeanDistance. Results: Experiment results demonstrated that the proposed methodobtained segmentation results for bilateral kidneys of 65 subjects with averageDice index of 0.9581, Jaccard index of 0.9204, and Mean Distance of 1.7166,better than other methods under comparison (p<10-19, paired Wilcoxon rank sumtests). Conclusions: The proposed method achieved promising performance forsegmenting kidneys in US images, better than segmentation methods that built onany single channel of image information. This method will facilitate extractionof kidney characteristics that may predict important clinical outcomes suchprogression chronic kidney disease.

A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images