Nonlinear holospectral imaging: scatter removal from curvilinear data in multidimensional energy space [nuclear medicine]

Author

Jouan, A. ; Laperrière, L. ; Gagnon, D.

Author_Institution

Dept. of Radiol., Montreal Univ., Que., Canada

fYear

1992

fDate

25-31 Oct 1992

Firstpage

1227

Abstract

After a brief review of the scatter removal technique in holospectral imaging, the authors describe its implementation, taking into account the nonlinear nature of the data. Qualitative results show that structures are better defined in the processed images when the scatter removal technique is applied locally after a segmentation of the multidimensional raw data. The proportion of the variance contained in the principal axis due to this curvilinear model is highly object-dependent and ranges from almost nothing (adequacy to linear model) to 5 to 8% in the worst case

Keywords

medical image processing; radioisotope scanning and imaging; curvilinear data; data segmentation; linear model; medical diagnostic imaging; multidimensional energy space; multidimensional raw data; nonlinear holospectral imaging; nuclear medicine; principal axis; scatter removal technique; Biomedical engineering; Biomedical imaging; Covariance matrix; Data mining; Feature extraction; Heart; Image sampling; Multidimensional systems; Scattering; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Nuclear Science Symposium and Medical Imaging Conference, 1992., Conference Record of the 1992 IEEE

Conference_Location

Orlando, FL

Print_ISBN

0-7803-0884-0

Type

conf

DOI

10.1109/NSSMIC.1992.301486

Filename

301486