18 March 2015 Interactive non-uniformity correction and intensity standardization of MR images
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Abstract
Image non-uniformity and intensity non-standardness are two major hurdles encountered in human and computer interpretation and analysis of magnetic resonance (MR) images. Automated methods for image non-uniformity correction (NC) and intensity standardization (IS) may fail because solutions for them require identifying regions representing the same tissue type for several different tissues, and the automatic strategies, irrespective of the approach, may fail in this task. This paper presents interactive strategies to overcome this problem: interactive NC and interactive IS. The methods require sample tissue regions to be specified for several different types of tissues. Interactive NC estimates the degree of non-uniformity at each voxel in a given image, builds a global function for non-uniformity correction, and then corrects the image to improve quality. Interactive IS includes two steps: a calibration step and a transformation step. In the first step, tissue intensity signatures of each tissue from a few subjects are utilized to set up key landmarks in a standardized intensity space. In the second step, a piecewise linear intensity mapping function is built between the same tissue signatures derived from the given image and those in the standardized intensity space to transform the intensity of the given image into standardized intensity. Preliminary results on abdominal T1-weighted and T2-weighted MR images of 20 subjects show that interactive NC and IS are feasible and can significantly improve image quality over automatic methods. Interactive IS for MR images combined with interactive NC can substantially improve numeric characterization of tissues.
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Yubing Tong, Yubing Tong, Jayaram K. Udupa, Jayaram K. Udupa, Dewey Odhner, Dewey Odhner, Shobhit Sharma, Shobhit Sharma, Drew A. Torigian, Drew A. Torigian, } "Interactive non-uniformity correction and intensity standardization of MR images", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 94151N (18 March 2015); doi: 10.1117/12.2082878; https://doi.org/10.1117/12.2082878
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