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Title: A computational model to generate simulated three-dimensional breast masses

Purpose: To develop algorithms for creating realistic three-dimensional (3D) simulated breast masses and embedding them within actual clinical mammograms. The proposed techniques yield high-resolution simulated breast masses having randomized shapes, with user-defined mass type, size, location, and shape characteristics. Methods: The authors describe a method of producing 3D digital simulations of breast masses and a technique for embedding these simulated masses within actual digitized mammograms. Simulated 3D breast masses were generated by using a modified stochastic Gaussian random sphere model to generate a central tumor mass, and an iterative fractal branching algorithm to add complex spicule structures. The simulated masses were embedded within actual digitized mammograms. The authors evaluated the realism of the resulting hybrid phantoms by generating corresponding left- and right-breast image pairs, consisting of one breast image containing a real mass, and the opposite breast image of the same patient containing a similar simulated mass. The authors then used computer-aided diagnosis (CAD) methods and expert radiologist readers to determine whether significant differences can be observed between the real and hybrid images. Results: The authors found no statistically significant difference between the CAD features obtained from the real and simulated images of masses with either spiculated or nonspiculated margins.more » Likewise, the authors found that expert human readers performed very poorly in discriminating their hybrid images from real mammograms. Conclusions: The authors’ proposed method permits the realistic simulation of 3D breast masses having user-defined characteristics, enabling the creation of a large set of hybrid breast images containing a well-characterized mass, embedded within real breast background. The computational nature of the model makes it suitable for detectability studies, evaluation of computer aided diagnosis algorithms, and teaching purposes.« less
; ; ;  [1] ;  [2] ;  [3]
  1. Medical Imaging Research Center, Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, Illinois 60616 (United States)
  2. Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, Chicago, Illinois 60637 (United States)
  3. Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213 (United States)
Publication Date:
OSTI Identifier:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 2; Other Information: (c) 2015 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States