A MAP based new interpolation method for medical images

Author

Wang, Cliff X. ; Snyder, Wesley E. ; Bilbro, Griff ; Santago, Pete, II

Author_Institution

Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA

Volume

3

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

1202

Abstract

Interpolation is very important in many medical imaging applications. The problems of interpolating low resolution medical images are approached in this paper as an optimization problem. The objective function is derived from Bayesian Maximum-a-posteriori (MAP) probability density, which effectively combines the measured low resolution image data with a-priori knowledge of the image property. Solution to the optimization problem produces a zoomed image which resembles the measured low resolution image, but has noise smoothed and blur corrected

Keywords

Bayes methods; image resolution; interpolation; medical image processing; optimisation; Bayesian maximum-a-posteriori probability density; a-priori knowledge; blur correction; image property; low resolution image data; low resolution medical images; medical diagnostic imaging; medical image interpolation method; optimization problem; smoothed noise; zoomed image; Biomedical imaging; Distortion measurement; Extrapolation; Image restoration; Interpolation; Kernel; Noise reduction; Pixel; Simulated annealing; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE

Conference_Location

Amsterdam

Print_ISBN

0-7803-3811-1

Type

conf

DOI

10.1109/IEMBS.1996.652773

Filename

652773