Urolithiasis is highly prevalent in the United States and precise stone measurement and skin to stone distance is critical in determining the most appropriate treatment modality. However, no specific measure is accepted as a gold standard for use in research or clinical care, as calculation of these individual parameters is difficult and time consuming.
We developed a stand-alone application in MATLAB (R2016b, MathWorks, Natick, MA) with a simple, easy-to-use interface which would allow to user to calculate several stone parameters from Computed Tomography (CT) images.
DICOM CT images are initially loaded. An attenuation threshold is incorporated into an automatic region growing algorithm and is utilized to identify stones on CT within a particular region of interest (ROI). An initial attenuation threshold of 250 Hounsfield units (HU) is selected to ensure the stone remains a single object during region growing, while eliminating adjacent soft-tissues. For accurate stone segmentation, the threshold is adjustable on the user interface according to the image segmentation result. The segmented binary images are obtained, followed by an automatic reconstruction using a marching cubes algorithm.
To measure distance from the stone to the nearest skin site, a seed point is manually placed outside the skin area and the skin contour is segneted automatically based on pixel intensity differences on the perimeter. The smallest distance from the stone to the skin perimeter is calculated automatically, and reported in the x- and y- directions of the image.
Instructions on using the software are available in the Help of the program.
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References:
Paper [837Kb]
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