Information-Theoretic Image Reconstruction and Segmentation from Noisy Projections

被引：0
|
作者：
Visser, Gerhard [1 ]
Dowe, David L. [1 ]
Svalbe, Imants D. [1 ]
机构：
[1] Monash Univ, Melbourne, Vic 3800, Australia
来源：
AI 2009: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2009年 / 5866卷
关键词：
INDUCTIVE INFERENCE; FORMAL THEORY; CLASSIFICATION; DISTRIBUTIONS; LENGTH;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The minimum message length (MML) principle for inductive inference has been successfully applied to image segmentation where the images are modelled by Markov random fields (MRF). We have extended this work to be capable of simultaneously reconstructing and segmenting images that have been observed only through noisy projections. The noise added to each projection depends on the classes of the pixels (material) that it passes through. The intended application is in low-dose (low-flux) X-ray computed tomography (CT) where irregular projections are used.
引用
收藏
页码：170 / 179
页数：10
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