Model-Based Region-of-Interest Selection in Dynamic Breast MRI

Abstract

Magnetic Resonance Imaging (MRI) is emerging as a powerful tool for the diagnosis of breast abnormalities. Dynamic analysis of the temporal pattern of contrast uptake has been applied in differential diagnosis of benign and malignant lesions to improve specificity. Selecting a region of interest "ROI" is an almost universal step in the process of examining the contrast uptake characteristics of a breast lesion. We propose an ROI selection method that combines modelbased clustering of the pixels with Bayesian morphology, a new statistical image segmentation method. We then investigate tools for subsequent analysis of signal intensity time course data in the selected region. Results on a data base of 19 patients are promising. The method provides informative segmentations and good detection rates are obtained.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2004
Accession Number
ADA478754

Entities

People

  • Adrian Raftery
  • Chris Fraley
  • David M. Goldhaber
  • Dianne Georgian-smith
  • Florence Forbes
  • Nathalie Peyrard

Organizations

  • University of Washington

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Breast Cancer
  • Clustering
  • Computer Vision
  • Contrast
  • Data Sets
  • Databases
  • Detection
  • Image Processing
  • Image Segmentation
  • Information Processing
  • Information Science
  • Intensity
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Neoplasms
  • Probability

Readers

  • Computer Vision.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML