19 March 2007 Point spread function based classification of regions for linear digital tomosynthesis
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Abstract
In digital tomosynthesis, one of the limitations is the presence of out-of-plane blur due to the limited angle acquisition. The point spread function (PSF) characterizes blur in the imaging volume, and is shift-variant in tomosynthesis. The purpose of this research is to classify the tomosynthesis imaging volume into four different categories based on PSF-driven focus criteria. We considered linear tomosynthesis geometry and simple back projection algorithm for reconstruction. The three-dimensional PSF at every pixel in the imaging volume was determined. Intensity profiles were computed for every pixel by integrating the PSF-weighted intensities contained within the line segment defined by the PSF, at each slice. Classification rules based on these intensity profiles were used to categorize image regions. At background and low-frequency pixels, the derived intensity profiles were flat curves with relatively low and high maximum intensities respectively. At in-focus pixels, the maximum intensity of the profiles coincided with the PSF-weighted intensity of the pixel. At out-of-focus pixels, the PSF-weighted intensity of the pixel was always less than the maximum intensity of the profile. We validated our method using human observer classified regions as gold standard. Based on the computed and manual classifications, the mean sensitivity and specificity of the algorithm were 77+/-8.44% and 91+/-4.13% respectively (t=-0.64, p=0.56, DF=4). Such a classification algorithm may assist in mitigating out-of-focus blur from tomosynthesis image slices.
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Kenny Israni, Kenny Israni, Gopal Avinash, Gopal Avinash, Baojun Li, Baojun Li, } "Point spread function based classification of regions for linear digital tomosynthesis", Proc. SPIE 6510, Medical Imaging 2007: Physics of Medical Imaging, 651050 (19 March 2007); doi: 10.1117/12.709493; https://doi.org/10.1117/12.709493
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