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

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.

[1]  Jeffrey C. Bamber,et al.  Performance criteria for quantitative ultrasonology and image parameterisation , 1990 .

[2]  C. R. Hill,et al.  Performance criteria for quantitative ultrasonography and image parameterisation. , 1990, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[3]  C. J. Kotre,et al.  The use of a contrast-detail test object in the optimization of optical density in mammography. , 1995, The British journal of radiology.

[4]  B. Tromberg,et al.  Non-invasive measurements of breast tissue optical properties using frequency-domain photon migration. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[5]  P M Schlag,et al.  Frequency-domain techniques enhance optical mammography: initial clinical results. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[6]  H. Barrett,et al.  Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  B. Pogue,et al.  Spatially variant regularization improves diffuse optical tomography. , 1999, Applied optics.

[8]  Brian W. Pogue,et al.  Confidence maps and confidence intervals for near infrared images in breast cancer , 1999, IEEE Transactions on Medical Imaging.

[9]  R. Huesman,et al.  Image properties of list mode likelihood reconstruction for a rectangular positron emission mammograph with DOI measurements , 2000, 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149).

[10]  K D Paulsen,et al.  Contrast-detail analysis for detection and characterization with near-infrared diffuse tomography. , 2000, Medical physics.

[11]  Britton Chance,et al.  Breast imaging technology: Probing physiology and molecular function using optical imaging - applications to breast cancer , 2000, Breast Cancer Research.

[12]  A. Hielscher,et al.  Instrumentation for fast functional optical tomography , 2002 .

[13]  B. Pogue,et al.  A parallel-detection frequency-domain near-infrared tomography system for hemoglobin imaging of the , 2001 .

[14]  Albert Cerussi,et al.  Noninvasive functional optical spectroscopy of human breast tissue , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[15]  B. Pogue,et al.  Quantitative hemoglobin tomography with diffuse near-infrared spectroscopy: pilot results in the breast. , 2001, Radiology.

[16]  B. Tromberg,et al.  Sources of absorption and scattering contrast for near-infrared optical mammography. , 2001, Academic radiology.

[17]  B. Pogue,et al.  Statistical analysis of nonlinearly reconstructed near-infrared tomographic images. I. Theory and simulations , 2002, IEEE Transactions on Medical Imaging.

[18]  B. Pogue,et al.  Statistical analysis of nonlinearly reconstructed near-infrared tomographic images. II. Experimental interpretation , 2002, IEEE Transactions on Medical Imaging.

[19]  Keith D. Paulsen,et al.  Statistical analysis of non-linearly reconstructed near-infrared tomographic images: Part II - Experimental studies , 2002, IEEE Trans. Medical Imaging.

[20]  Brian W. Pogue,et al.  Statistical analysis of non-linearly reconstructed near-infrared tomographic images: Part I - Theory and simulations , 2002, IEEE Trans. Medical Imaging.

[21]  Brian W Pogue,et al.  Quantitative analysis of near-infrared tomography: sensitivity to the tissue-simulating precalibration phantom. , 2003, Journal of biomedical optics.

[22]  Brian W. Pogue,et al.  Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[23]  K. Paulsen,et al.  Thresholds for detecting and characterizing focal lesions using steady-state MR elastography. , 2003, Medical physics.

[24]  P. Sheng,et al.  Theory and Simulations , 2003 .

[25]  B. Pogue,et al.  Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation, phantom, and clinical results. , 2003, Applied optics.

[26]  Matthew A. Kupinski,et al.  Objective Assessment of Image Quality , 2005 .