Translator Disclaimer
24 February 2012 Blind local noise estimation for medical images reconstructed from rapid acquisition
Author Affiliations +
Developments in rapid acquisition techniques and reconstruction algorithms, such as sensitivity encoding (SENSE) for MR images and fan-beam filtered backprojection (fFBP) for CT images, have seen widely applications in medical imaging in recent years. Nevertheless, such techniques introduce spatially varying noise levels in the reconstructed medical images that may degrade the image quality and hinder subsequent diagnostic inspection. Though this may be alleviated with multiple scanning images or the sensitivity profiles of imaging device, these pieces of information are typically unavailable in clinical practice. In this work, we describe a novel local noise level estimation technique based on the near constancy of kurtosis of medical image in band-pass filtered domain. This technique can effectively estimate noise levels in the pixel domain and recover the noise map for reconstructed medical images with nonuniform noise distribution. The advantage of this method is that it requires no prior knowledge of the imaging devices and can be implemented when only one single medical image is available. We report experiments that demonstrate the effectiveness of the proposed method in estimating the local noise levels for medical images quantitatively and qualitatively, and compare its estimation performance to another recent developed blind noise estimation approach. Finally, we also evaluate the practical denoising performance of our noise estimation algorithm on medical images when it is used as a front-end to a denoiser that uses principal component analysis with local pixel grouping (LPG-PCA) technique.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xunyu Pan, Xing Zhang, and Siwei Lyu "Blind local noise estimation for medical images reconstructed from rapid acquisition", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143R (24 February 2012);

Back to Top