Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

Xiaofeng Yang, Shengyong Wu, Ioannis Sechopoulos, Baowei Fei

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6 ± 2.0. Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Original languageEnglish (US)
Pages (from-to)6397-6406
Number of pages10
JournalMedical physics
Volume39
Issue number10
DOIs
StatePublished - Oct 2012
Externally publishedYes

Keywords

  • breast CT
  • breast cancer
  • cupping artifact correction
  • fuzzy C-means
  • image classification
  • mammography
  • multiscale filter
  • segmentation

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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