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At the time the article was created Candace Makeda Moore had no recorded disclosures.View Candace Makeda Moore's current disclosures
At the time the article was last revised Daniel J Bell had no recorded disclosures.View Daniel J Bell's current disclosures
Segmentation, in the context of informatics for radiology, refers to the delineation of areas of interest in imaging in terms of pixels or voxels. Segmentation is often accomplished by computerized algorithms that vary in complexity from simply selecting pixels of similar values in proximity to those that include radiometric features or are based on machine learning. The underlying algorithms that can be used for segmentation include, but are not limited to thresholding (simply taking all the pixels within a certain value range), K-means and Otsu's algorithm. In order to evaluate segmentation algorithms researchers have used ROC analysis, odds ratios, the superiority evaluation method, the deviation evaluation model, Hausdorff distances, contour mean distances, regression analysis, volumetric differences and Dice similarity coefficients to compare segmentations to expert segmented gold standards or statistically generated gold standards.
Several software programs can be used to segment radiological images, usually but not exclusively CT or MRI. Here follows a list of segmentation tools optimized for radiological images that are free and open-source:
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