Title: A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images,
Abstract: Purpose: To improve kidney segmentation in clinical ultrasound (US) images,
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. Methods: 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 GC based segmentation iteratively progresses
until convergence. The proposed method has been evaluated and compared with
state of the art image segmentation methods based on clinical kidney US images
of 85 subjects. We randomly selected US images of 20 subjects as training data
for tuning the parameters, and validated the methods based on US images of the
remaining 65 subjects. The segmentation results have been quantitatively
analyzed using 3 metrics, including Dice Index, Jaccard Index, and Mean
Distance. Results: Experiment results demonstrated that the proposed method
obtained segmentation results for bilateral kidneys of 65 subjects with average
Dice 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 sum
tests). Conclusions: The proposed method achieved promising performance for
segmenting kidneys in US images, better than segmentation methods that 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.