Frequency-Domain Optical Mammogram

Abstract

This research project has involved the analysis of a clinical data set of frequency-domain optical mammograms (-150 patients) to maximize the effectiveness of this breast imaging modality for cancer detection and to guide further improvements of this technique. During the first year, we have computed edge-corrected optical mammograms (N-images) for the whole data set and obtained the corresponding ROC curve. During the second year, we have devised a new approach to tumor oximetry on the basis of spectral data, and we have started developing a second-derivative scheme of image processing. During the third year, we have finalized the second-derivative processing scheme and applied it to the whole set of clinical data. By combining the information provided by the second- derivative optical mammograms at one wavelength, and the oxygenation images computed from data at four wavelengths, we have obtained a new ROC curve that shows a significant improvement upon the ROC curve based on the N-images alone. In summary, we have completed our broad objectives: (1) maximize the clinically useful information extracted from the existing set of optical mammograms; (2) guide further developments of optical mammography by introducing a new approach to tumor oximetry.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2002
Accession Number
ADB287619

Entities

People

  • Sergio Fantini

Organizations

  • Tufts University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Bioengineering
  • Cardiovascular Physiological Phenomena
  • Detection
  • Detectors
  • Diagnostic Imaging
  • Electrical Engineering
  • Health Services
  • Image Processing
  • Lasers
  • Medical Personnel
  • Neuroimaging
  • Optical Properties
  • Optics
  • Refractive Index
  • Tomography
  • Two Dimensional
  • Veins

Fields of Study

  • Medicine
  • Physics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Oncology and Biomarker-Based Cancer Detection.
  • Trauma or Military Medicine

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference