Model-based reconstruction of multiple circular and elliptical objects from a limited number of projections

S. Wang; B. Liu; S. R. Kulkarni

doi:10.1109/83.535853

Model-based reconstruction of multiple circular and elliptical objects from a limited number of projections

S. Wang, B. Liu, S. R. Kulkarni

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We consider tomographic image reconstruction from a limited number of noisy projections. An efficient algorithm based on maximum likelihood estimation (MLE) is developed to reconstruct images of multiple discs with unknown locations and radii. The algorithm is successfully applied to images with signal-to-noise ratio (SNR) as low as 0 dB, using as few as 16 projections, and containing as many as twelve discs with widely varying radii. Experimental results show that our approach significantly outperforms conventional convolution back projection. The algorithm is successfully extended to the multiple ellipse case.

Original language	English (US)
Pages (from-to)	1386-1390
Number of pages	5
Journal	IEEE Transactions on Image Processing
Volume	5
Issue number	9
DOIs	https://doi.org/10.1109/83.535853
State	Published - 1996

All Science Journal Classification (ASJC) codes

Software
Computer Graphics and Computer-Aided Design

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10.1109/83.535853

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title = "Model-based reconstruction of multiple circular and elliptical objects from a limited number of projections",

abstract = "We consider tomographic image reconstruction from a limited number of noisy projections. An efficient algorithm based on maximum likelihood estimation (MLE) is developed to reconstruct images of multiple discs with unknown locations and radii. The algorithm is successfully applied to images with signal-to-noise ratio (SNR) as low as 0 dB, using as few as 16 projections, and containing as many as twelve discs with widely varying radii. Experimental results show that our approach significantly outperforms conventional convolution back projection. The algorithm is successfully extended to the multiple ellipse case.",

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N2 - We consider tomographic image reconstruction from a limited number of noisy projections. An efficient algorithm based on maximum likelihood estimation (MLE) is developed to reconstruct images of multiple discs with unknown locations and radii. The algorithm is successfully applied to images with signal-to-noise ratio (SNR) as low as 0 dB, using as few as 16 projections, and containing as many as twelve discs with widely varying radii. Experimental results show that our approach significantly outperforms conventional convolution back projection. The algorithm is successfully extended to the multiple ellipse case.

AB - We consider tomographic image reconstruction from a limited number of noisy projections. An efficient algorithm based on maximum likelihood estimation (MLE) is developed to reconstruct images of multiple discs with unknown locations and radii. The algorithm is successfully applied to images with signal-to-noise ratio (SNR) as low as 0 dB, using as few as 16 projections, and containing as many as twelve discs with widely varying radii. Experimental results show that our approach significantly outperforms conventional convolution back projection. The algorithm is successfully extended to the multiple ellipse case.

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Model-based reconstruction of multiple circular and elliptical objects from a limited number of projections

Abstract

All Science Journal Classification (ASJC) codes

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