Post-Processing of Low Dose Mammography Images

Abstract

In mammography, X-ray radiation is used in sufficient doses to be captured on film for cancer diagnosis. A problem lies in the inherent nature of X-rays to cause cancer. The resolution of the images obtained on film is directly related to the radiation dosage. Thus, a trade off between image quality and radiation exposure is necessary to ensure proper diagnosis without causing cancer. A possible solution is to decrease the dosage of radiation and improve the image quality of mammograms using post- processing methods applied to digitized film images. Image processing techniques that may improve the resolution of images captured at lower doses include crispening, denoising, histogram equalization, and pattern recognition methods. The Wright Patterson Air Force Base Hospital Radiology Department sponsored this research and provided digitized images of the American College of Radiology (ACR) phantom, which is a model for mammogram image quality and classification. Side by side comparisons were performed of high dose images and low-dose images post-processed using the methods mentioned. The result was improved- resolution on mammography images for lower radiation doses. Thus, this research represents progress towards solving a problem that currently plagues mammography: exposure of patients to high doses of cancer- causing radiation to obtain quality mammography images. By improving the image quality of mammography images at lower radiation doses, the problem of cancer induced by high radiation exposure is alleviated.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2002
Accession Number
ADA409113

Entities

People

  • Jesung Kim

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Change Detection
  • Detection
  • Detectors
  • Electrical Engineering
  • Engineering
  • Health Care
  • Health Services
  • Image Processing
  • Mammography
  • Medical Personnel
  • Pattern Recognition
  • Recognition
  • Scattering
  • Two Dimensional
  • X Rays

Fields of Study

  • Medicine
  • Physics

Readers

  • Image Processing and Computer Vision.
  • Nuclear and Radiation Engineering.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML