TY - GEN
T1 - Mean-variance blind noise estimation for CT images
AU - James, Alex Pappachen
AU - Kavitha, A. P.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Noise estimation is a precursor to de-noising techniques to improve the signal and visual quality of medical images. We present a noise estimation algorithm using the local image statistics of the CT images at voxel level. The algorithm calculates the local mean variance distribution and detects the minimised error rates for identifying the tolerance range of voxel to artificial noises. The reliability of the method is experimentally verified using Gaussian noise and Speckle noise on CT scan images.
AB - Noise estimation is a precursor to de-noising techniques to improve the signal and visual quality of medical images. We present a noise estimation algorithm using the local image statistics of the CT images at voxel level. The algorithm calculates the local mean variance distribution and detects the minimised error rates for identifying the tolerance range of voxel to artificial noises. The reliability of the method is experimentally verified using Gaussian noise and Speckle noise on CT scan images.
UR - http://www.scopus.com/inward/record.url?scp=84940277568&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84940277568&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-04960-1_21
DO - 10.1007/978-3-319-04960-1_21
M3 - Conference contribution
AN - SCOPUS:84940277568
SN - 9783319049595
T3 - Advances in Intelligent Systems and Computing
SP - 235
EP - 243
BT - Advances in Signal Processing and Intelligent Recognition Systems
PB - Springer Verlag
T2 - International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2014
Y2 - 13 March 2014 through 15 March 2014
ER -