Automated region detection based on the contrast-to-noise ratio in near-infrared tomography

Xiaomei Song; Brian W. Pogue; Shudong Jiang; Marvin M. Doyley; Hamid Dehghani; Tor D. Tosteson; Keith D. Paulsen

doi:10.1364/AO.43.001053

Automated region detection based on the contrast-to-noise ratio in near-infrared tomography

Xiaomei Song^*, Brian W. Pogue, Shudong Jiang, Marvin M. Doyley, Hamid Dehghani, Tor D. Tosteson, Keith D. Paulsen

^*Corresponding author for this work

Computer Science

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

Abstract

The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest (ROI) detection.

Original language	English
Pages (from-to)	1053-1062
Number of pages	10
Journal	Applied Optics
Volume	43
Issue number	5
DOIs	https://doi.org/10.1364/AO.43.001053
Publication status	Published - 10 Feb 2004

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Engineering (miscellaneous)
Electrical and Electronic Engineering

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title = "Automated region detection based on the contrast-to-noise ratio in near-infrared tomography",

abstract = "The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest (ROI) detection.",

author = "Xiaomei Song and Pogue, {Brian W.} and Shudong Jiang and Doyley, {Marvin M.} and Hamid Dehghani and Tosteson, {Tor D.} and Paulsen, {Keith D.}",

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AU - Song, Xiaomei

AU - Pogue, Brian W.

AU - Jiang, Shudong

AU - Doyley, Marvin M.

AU - Dehghani, Hamid

AU - Tosteson, Tor D.

AU - Paulsen, Keith D.

PY - 2004/2/10

Y1 - 2004/2/10

N2 - The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest (ROI) detection.

AB - The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest (ROI) detection.

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SN - 1559-128X

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EP - 1062

JO - Applied Optics

JF - Applied Optics

IS - 5

ER -

Automated region detection based on the contrast-to-noise ratio in near-infrared tomography

Abstract

ASJC Scopus subject areas

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