Abstract: Noise assessment is important to image quality evaluation as well as image processing. For example, the noise level estimation is used as criteria for terminating an iterative noise reduction process. To determine a meaningful noise level, the pixels in featureless regions are separated from the rest of the image. A new concept of pseudo-standard deviation (PSD) is introduced to automatically determine simple and reliable noise level estimates. Furthermore, a histogram of PSD is constructed with fine bins to calculate the moving average of the histogram. The first peak in filtered histogram gives the most representative noise measure as a desired approximation of true standard deviation.

Abstract: Noise assessment is important to image quality evaluation as well as image processing. For example, the noise level estimation is used as criteria for terminating an iterative noise reduction process. To determine a meaningful noise level, the pixels in featureless regions are separated from the rest of the image. A new concept of pseudo-standard deviation (PSD) is introduced to automatically determine simple and reliable noise level estimates. Furthermore, a histogram of PSD is constructed with fine bins to calculate the moving average of the histogram. The first peak in filtered histogram gives the most representative noise measure as a desired approximation of true standard deviation.

Abstract: Three or more materials are advantageously separated from dual energy data by using a material separation technique. To effectively separate material clusters, a density plot is introduced to automatically render cluster separations. Initially, the projection data optionally undergo data-domain dual energy decomposition. Then, the image data is plotted in a vector plot whose axes are the low HU values and the high HU values. For a given data point in the vector plot, a number of data points is counted within in a region of interest surrounding the given data point to generate a density plot where each point now represents a density level surrounding the data point. Thus, clustering of a certain material is visualized by a predetermined color assignment scheme. Furthermore, special image processing methods such as Gaussian decomposition are used to improve the accuracy of material separation.

Abstract: Three or more materials are advantageously separated from dual energy data by using a material separation technique. To effectively separate material clusters, a density plot is introduced to automatically render cluster separations. Initially, the projection data optionally undergo data-domain dual energy decomposition. Then, the image data is plotted in a vector plot whose axes are the low HU values and the high HU values. For a given data point in the vector plot, a number of data points is counted within in a region of interest surrounding the given data point to generate a density plot where each point now represents a density level surrounding the data point. Thus, clustering of a certain material is visualized by a predetermined color assignment scheme. Furthermore, special image processing methods such as Gaussian decomposition are used to improve the accuracy of material separation.