Histogram derived penalty functions in gradient-based optical tomography

Andreas Hielscher; Sebastian Bartel

Histogram derived penalty functions in gradient-based optical tomography

Andreas Hielscher, Sebastian Bartel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

It is well known that the image reconstruction problem in optical tomography is ill-posed. In this work we approach the problem within a gradient-based image iterative reconstruction (GIIR) scheme. The reconstruction is considered as a minimization of an objective function. This function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that contain a priori information about the system. In this work penalty functions are considered that are derived from full or partial knowledge of the histogram of the image to be reconstructed.

Original language	English (US)
Title of host publication	Biomedical Optical Spectroscopy and Diagnostics, T. Li, ed., Vol. 38 of OSA Trends in Optics and Photonics (Optica Publishing Group, 2000)
State	Published - 2000

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title = "Histogram derived penalty functions in gradient-based optical tomography",

abstract = "It is well known that the image reconstruction problem in optical tomography is ill-posed. In this work we approach the problem within a gradient-based image iterative reconstruction (GIIR) scheme. The reconstruction is considered as a minimization of an objective function. This function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that contain a priori information about the system. In this work penalty functions are considered that are derived from full or partial knowledge of the histogram of the image to be reconstructed.",

author = "Andreas Hielscher and Sebastian Bartel",

year = "2000",

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T1 - Histogram derived penalty functions in gradient-based optical tomography

AU - Hielscher, Andreas

AU - Bartel, Sebastian

PY - 2000

Y1 - 2000

N2 - It is well known that the image reconstruction problem in optical tomography is ill-posed. In this work we approach the problem within a gradient-based image iterative reconstruction (GIIR) scheme. The reconstruction is considered as a minimization of an objective function. This function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that contain a priori information about the system. In this work penalty functions are considered that are derived from full or partial knowledge of the histogram of the image to be reconstructed.

AB - It is well known that the image reconstruction problem in optical tomography is ill-posed. In this work we approach the problem within a gradient-based image iterative reconstruction (GIIR) scheme. The reconstruction is considered as a minimization of an objective function. This function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that contain a priori information about the system. In this work penalty functions are considered that are derived from full or partial knowledge of the histogram of the image to be reconstructed.

M3 - Conference contribution

BT - Biomedical Optical Spectroscopy and Diagnostics, T. Li, ed., Vol. 38 of OSA Trends in Optics and Photonics (Optica Publishing Group, 2000)

ER -

Histogram derived penalty functions in gradient-based optical tomography

Abstract

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