Title :
Tomographic reconstruction based on flexible geometric models
Author :
Hanson, K.M. ; Cunningham, G.S. ; Jennings, G.R., Jr. ; Wolf, Jr D R
Author_Institution :
Los Alamos Nat. Lab., NM, USA
Abstract :
When dealing with ill-posed inverse problems in data analysis, the Bayesian approach allows one to use prior information to guide the result toward reasonable solutions. In this work the model consists of an object whose amplitude is constant inside a flexible boundary. The flexibility of the boundary is controlled by through a distortion energy. We present an example of reconstruction of the cross section of a blood vessel from just two projections
Keywords :
Bayes methods; blood; computerised tomography; data analysis; image reconstruction; inverse problems; medical image processing; Bayesian approach; blood vessel cross section; computed tomography; data analysis; distortion energy; flexible geometric models; ill-posed inverse problems; image reconstruction; tomographic reconstruction; Bayesian methods; Biomedical imaging; Blood vessels; Image reconstruction; Noise measurement; Pixel; Probability density function; Shape measurement; Solid modeling; Tomography;
Conference_Titel :
Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference
Conference_Location :
Austin, TX
Print_ISBN :
0-8186-6952-7
DOI :
10.1109/ICIP.1994.413548
