### Abstract

In two-dimensional image reconstruction from line integrals using maximum likelihood, Bayesian, or minimum variance algorithms, the x-y plane on which the object estimate is defined is decomposed into nonoverlapping regions, or ″pixels″ . This decompostion of an otherwise continuous structure results in significant errors, or model noise, which can exceed the effects of the fundamental measurement noise. Many applications of diagnostic cross-sectional imaging require that images be obtained from limited data. A new formalism provides a connection between the continuous object to be reconstructed and its discrete representation. Using this formalism, the authors describe a decomposition of the x-y plane into a set of discrete, but overlapping pixels. In this model the pixels are uniquely defined by the beam paths. These ″natural″ pixels provide new, discrete filtered back projection reconstruction algorithms for limited data. The resulting reconstruction system not only avoids the pixel partitioning error, but provides basic mathematical properties which lead to profound computational advantages.

Original language | English (US) |
---|---|

Pages (from-to) | 69-78 |

Number of pages | 10 |

Journal | IEEE Transactions on Biomedical Engineering |

Volume | BME-28 |

Issue number | 2 |

State | Published - Feb 1981 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Biomedical Engineering

### Cite this

*IEEE Transactions on Biomedical Engineering*,

*BME-28*(2), 69-78.

**NATURAL PIXEL DECOMPOSITION FOR TWO-DIMENSIONAL IMAGE RECONSTRUCTION.** / Buonocore, Michael H.; Brody, William R.; Macovski, Albert.

Research output: Contribution to journal › Article

*IEEE Transactions on Biomedical Engineering*, vol. BME-28, no. 2, pp. 69-78.

}

TY - JOUR

T1 - NATURAL PIXEL DECOMPOSITION FOR TWO-DIMENSIONAL IMAGE RECONSTRUCTION.

AU - Buonocore, Michael H.

AU - Brody, William R.

AU - Macovski, Albert

PY - 1981/2

Y1 - 1981/2

N2 - In two-dimensional image reconstruction from line integrals using maximum likelihood, Bayesian, or minimum variance algorithms, the x-y plane on which the object estimate is defined is decomposed into nonoverlapping regions, or ″pixels″ . This decompostion of an otherwise continuous structure results in significant errors, or model noise, which can exceed the effects of the fundamental measurement noise. Many applications of diagnostic cross-sectional imaging require that images be obtained from limited data. A new formalism provides a connection between the continuous object to be reconstructed and its discrete representation. Using this formalism, the authors describe a decomposition of the x-y plane into a set of discrete, but overlapping pixels. In this model the pixels are uniquely defined by the beam paths. These ″natural″ pixels provide new, discrete filtered back projection reconstruction algorithms for limited data. The resulting reconstruction system not only avoids the pixel partitioning error, but provides basic mathematical properties which lead to profound computational advantages.

AB - In two-dimensional image reconstruction from line integrals using maximum likelihood, Bayesian, or minimum variance algorithms, the x-y plane on which the object estimate is defined is decomposed into nonoverlapping regions, or ″pixels″ . This decompostion of an otherwise continuous structure results in significant errors, or model noise, which can exceed the effects of the fundamental measurement noise. Many applications of diagnostic cross-sectional imaging require that images be obtained from limited data. A new formalism provides a connection between the continuous object to be reconstructed and its discrete representation. Using this formalism, the authors describe a decomposition of the x-y plane into a set of discrete, but overlapping pixels. In this model the pixels are uniquely defined by the beam paths. These ″natural″ pixels provide new, discrete filtered back projection reconstruction algorithms for limited data. The resulting reconstruction system not only avoids the pixel partitioning error, but provides basic mathematical properties which lead to profound computational advantages.

UR - http://www.scopus.com/inward/record.url?scp=0019394026&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0019394026&partnerID=8YFLogxK

M3 - Article

VL - BME-28

SP - 69

EP - 78

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 2

ER -