This work presents an improved algorithm for the generation of 3D breast software phantoms and its evaluation for mammography.Methods:
The improved methodology has evolved from a previously presented 3D noncompressed breast modeling method used for the creation of breast models of different size, shape, and composition. The breast phantom is composed of breast surface, duct system and terminal ductal lobular units, Cooper’s ligaments, lymphatic and blood vessel systems, pectoral muscle, skin, 3D mammographic background texture, and breast abnormalities. The key improvement is the development of a new algorithm for 3D mammographic texture generation. Simulated images of the enhanced 3D breast model without lesions were produced by simulating mammographicimage acquisition and were evaluated subjectively and quantitatively. For evaluation purposes, a database with regions of interest taken from simulated and real mammograms was created. Four experienced radiologists participated in a visual subjective evaluation trial, as they judged the quality of the simulated mammograms, using the new algorithm compared to mammograms, obtained with the old modeling approach. In addition, extensive quantitative evaluation included power spectral analysis and calculation of fractal dimension, skewness, and kurtosis of simulated and real mammograms from the database.Results:
The results from the subjective evaluation strongly suggest that the new methodology for mammographic breast texture creates improved breast models compared to the old approach. Calculated parameters on simulated images such as exponent deducted from the power law spectral analysis and fractal dimension are similar to those calculated on real mammograms. The results for the kurtosis and skewness are also in good coincidence with those calculated from clinical images. Comparison with similar calculations published in the literature showed good agreement in the majority of cases.Conclusions:
The improved methodology generated breast models with increased realism compared to the older model as shown in evaluations of simulated images by experienced radiologists. It is anticipated that the realism will be further improved using an advanced image simulator so that simulated images may be used in feasibility studies in mammography.
The contributions by Sankar Suryanarayanan and Andrew Karellas were supported in part by the National Institutes of Health (NIH) Grant No. R01-EB002123, Grant No. R01-EB004015 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and from the Georgia Cancer Coalition. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH, NIBIB, or the Georgia Cancer Coalition. The authors would like also to thank Dr. George Karatzas for providing mammograms of women who underwent routine screening mammographic examination in Greece and his fruitful suggestions and comments.
II. MATERIALS AND METHODS
II.A. Breast model
II.B. Simulation of projection images
III.A.1. Clinical images
III.A.2. Synthetic images
III.B. Subjective evaluation
III.C. Objective evaluation
III.C.1. Skewness and kurtosis
III.C.2. Power law spectral analysis
III.C.3. Fractal dimension
IV.A. Subjective evaluation
IV.B. Objective evaluation
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