Joint-MAP reconstruction/segmentation for transmission tomography using mixture-models as priors

Ing Tsung Hsiao*, Anand Rangarajan, Gene Gindi

*此作品的通信作者

研究成果: 圖書/報告稿件的類型會議稿件同行評審

13 引文 斯高帕斯(Scopus)

摘要

A Bayesian method, including a pointwise prior comprising mixtures of gamma distributions, is applied to the problem of transmission tomography. A joint MAP (maximum a posteriori) procedure is proposed wherein the reconstruction itself, as well as all pointwise parameters, are calculated simultaneously. It uses an algorithm that successively refines the estimate of the mixture parameters and the reconstruction. The approach aims to solve the problem of low counts statistics in transmission tomography. Initial simulation results with anecdotal reconstruction show that the gamma mixture model likely outperforms the ML (maximum likelihood) method and FBP (filtered-backprojection) algorithm.

原文英語
主出版物標題IEEE Nuclear Science Symposium and Medical Imaging Conference
發行者IEEE
頁面1689-1693
頁數5
ISBN(列印)0780350227
出版狀態已出版 - 1999
對外發佈
事件Proceedings of the 1998 IEEE Nuclear Science Symposium Conference Record - Toronto, Que, Can
持續時間: 08 11 199814 11 1998

出版系列

名字IEEE Nuclear Science Symposium and Medical Imaging Conference
3

Conference

ConferenceProceedings of the 1998 IEEE Nuclear Science Symposium Conference Record
城市Toronto, Que, Can
期間08/11/9814/11/98

指紋

深入研究「Joint-MAP reconstruction/segmentation for transmission tomography using mixture-models as priors」主題。共同形成了獨特的指紋。

引用此