Automatic tissue classification for high-resolution breast CT images based on bilateral filtering

Xiaofeng Yang, Ioannis Sechopoulos, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations


Breast tissue classification can provide quantitative measurements of breast composition, density and tissue distribution for diagnosis and identification of high-risk patients. In this study, we present an automatic classification method to classify high-resolution dedicated breast CT images. The breast is classified into skin, fat and glandular tissue. First, we use a multiscale bilateral filter to reduce noise and at the same time keep edges on the images. As skin and glandular tissue have similar CT values in breast CT images, we use morphologic operations to get the mask of the skin based on information of its position. Second, we use a modified fuzzy C-mean classification method twice, one for the skin and the other for the fatty and glandular tissue. We compared our classified results with manually segmentation results and used Dice overlap ratios to evaluate our classification method. We also tested our method using added noise in the images. The overlap ratios for glandular tissue were above 94.7% for data from five patients. Evaluation results showed that our method is robust and accurate.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2011
Subtitle of host publicationImage Processing
StatePublished - 2011
Externally publishedYes
EventMedical Imaging 2011: Image Processing - Lake Buena Vista, FL, United States
Duration: Feb 14 2011Feb 16 2011

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2011: Image Processing
Country/TerritoryUnited States
CityLake Buena Vista, FL


  • Breast CT
  • bias correction
  • breast cancer
  • breast tissue classification
  • fuzzy C-Mean classification
  • image classification
  • multiscale filter

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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