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Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: Application for mammography
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10.1118/1.3491812
/content/aapm/journal/medphys/37/11/10.1118/1.3491812
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/11/10.1118/1.3491812

Figures

Image of FIG. 1.
FIG. 1.

The main steps in composing the mammographic texture. The images are slices extracted from a breast matrix with size of 75 mm in each direction and voxel dimension of . The texture matrix was created using the following parameters: Number of random walks , number of steps per walk , increment , and Hurst exponent . Additionally, a mask was used to perform the low pass filtering and dilation, while the standard deviation value for the Gaussian filtering was set to 1.5. For better understanding, the names of the matrices obtained after the steps are related with the imaging operation performed, namely, RWM: Random walk matrix; DM: Dilation matrix; LPM: Low pass filtering matrix; GM: Gaussian matrix; and FTM: Final texture matrix.

Image of FIG. 2.
FIG. 2.

Algorithm for random walks generation. and are the number of three-dimensional random walks and the corresponding random steps per walk.

Image of FIG. 3.
FIG. 3.

Example of defining ROIs on a mammogram.

Image of FIG. 4.
FIG. 4.

Examples of simulated mammograms at CC and MLO view obtained from [(a)–(d)] fatty breast models, [(e)–(i)] models of glandular breasts, and [(j) and (l)] dense breast models.

Image of FIG. 5.
FIG. 5.

Examples of simulated mammograms with [(a)–(c)] the new and [(d)–(f)] the old algorithm for 3D mammographic texture.

Image of FIG. 6.
FIG. 6.

Examples of [(a)–(f)] real and [(g)–(l)] simulated ROIs used in the evaluation.

Image of FIG. 7.
FIG. 7.

(a) Examples of ROIs and their corresponding calculated exponent, skewness (s), kurtosis (k), and fractal dimension (FD) from a simulated image; a clinical mammogram obtained with (b) GE Senograph 2000D and (c) taken form MIAS Mini Database mdb007.

Image of FIG. 8.
FIG. 8.

Distribution of values, correlation coefficients from the linear regression analysis, as well as distribution of skewness and kurtosis for ROIs extracted from (a) simulated images and real mammograms taken from (b) a private patient database and (c) MIAS MiniDatabase.

Tables

Generic image for table
TABLE I.

Attenuation coefficients at 19 keV incident energy. In the same table, the lower and upper limits of the HUs for the simulated tissues are specified.

Generic image for table
TABLE II.

Default values for the creation of breast models of different glandularity and dimensions. F: Fatty breast; G: Glandular breast; D: Dense breast matrix size is in three dimensions.

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TABLE III.

Results from the radiologists’ evaluation. A three grade scale is used in the evaluation. Grade 1: Images are not realistic at all, grade 2: Images of medium realism, and grade 3: Images look like real mammograms.

Generic image for table
TABLE IV.

Results from the quantitative evaluation and comparison with results from literature.

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/content/aapm/journal/medphys/37/11/10.1118/1.3491812
2010-10-06
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: Application for mammography
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/11/10.1118/1.3491812
10.1118/1.3491812
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