Automated Assessment of the Composition of Breast Tissue Revealed on Tissue-Thickness-Corrected Mammography

Automated Assessment of the Composition of Breast Tissue Revealed on Tissue-Thickness-Corrected Mammography

Xiao Hui Wang¹, Walter F. Good, Brian E. Chapman, Yuan-Hsiang Chang, William R. Poller, Thomas S. Chang and Lara A. Hardesty

^¹ All authors: Department of Radiology, Imaging Research, Ste. 4200, University of Pittsburgh and Magee-Womens Hospital of the University of Pittsburgh Medical Center Health System, 300 Halket St., Pittsburgh, PA 15213-3180.

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Fig. 1. —Bar graph shows distribution of breast tissue composition in database of mammograms used in study. For classifications of breast tissue composition, 1 = almost entirely fat, 2 = scattered fibroglandular densities, 3 = heterogeneously dense, and 4 = extremely dense.

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Fig. 2. —Sigmoid curve in graph is combined characteristic function curve of film and film digitizer. Dashed line illustrates relationship of energy exposure and radiographic intensity after linearization of function curve.

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Fig. 3. —Function curve in graph shows thickness index, represented by normalized pixel value, as function of distance from skin line. Solid line represents estimated mean change in optical density for pure fat pixels, with respect to distance from skin line. Optical density values increase toward skin line as thickness of breast tissue decreases. Corresponding fitted function is represented by dashed line.

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Fig. 4. —Diagram illustrates structure of neural network classifier used in study: an input layer with four inputs (histographic features identified in text), a hidden layer with three nodes, and one output node.

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Fig. 5A. —Two sets of images illustrate effect of image thickness correction. Original mammogram of 58-year-old woman.

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Fig. 5B. —Two sets of images illustrate effect of image thickness correction. Thickness-corrected image of A.

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Fig. 5C. —Two sets of images illustrate effect of image thickness correction. Original mammogram of 56-year-old woman.

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Fig. 5D. —Two sets of images illustrate effect of image thickness correction. Thickness-corrected image of C.

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Fig. 6A. —Thickness-corrected images paired with original mammograms illustrate applicability of tissue-thickness-correction algorithms in various tissue types. Thickness-corrected image of breast of 72-year-old woman shows tissue that is almost entirely fat. Compare with corresponding mammogram (E).

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Fig. 6B. —Thickness-corrected images paired with original mammograms illustrate applicability of tissue-thickness-correction algorithms in various tissue types. Thickness-corrected image of breast of 56-year-old woman reveals tissue with scattered fibroglandular densities. Compare with corresponding mammogram (F).

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Fig. 6C. —Thickness-corrected images paired with original mammograms illustrate applicability of tissue-thickness-correction algorithms in various tissue types. Thickness-corrected image of breast of 49-year-old woman shows heterogeneously dense tissue. Compare with corresponding mammogram (G).

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Fig. 6D. —Thickness-corrected images paired with original mammograms illustrate applicability of tissue-thickness-correction algorithms in various tissue types. Thickness-corrected image of breast of 42-year-old woman reveals extremely dense tissue. Compare with corresponding mammogram (H).

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Fig. 6E. —Thickness-corrected images paired with original mammograms illustrate applicability of tissue-thickness-correction algorithms in various tissue types. Original mammograms corresponding to A—D.

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Fig. 6F. —Thickness-corrected images paired with original mammograms illustrate applicability of tissue-thickness-correction algorithms in various tissue types. Original mammograms corresponding to A—D.

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Fig. 6G. —Thickness-corrected images paired with original mammograms illustrate applicability of tissue-thickness-correction algorithms in various tissue types. Original mammograms corresponding to A—D.

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Fig. 6H. —Thickness-corrected images paired with original mammograms illustrate applicability of tissue-thickness-correction algorithms in various tissue types. Original mammograms corresponding to A—D.

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Fig. 7A. —Graphs show accuracy of results of tissue composition assessment by neural network classifier. Percentage of correctly classified images was used as measure of network's performance. Neural network based on corrected images performed better than that based on uncorrected images. ${blacksquare}$ = thickness-corrected images; ${blacktriangleup}$ = original images. Graphs illustrate results from training (A) and testing (B) sets.

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Fig. 7B. —Graphs show accuracy of results of tissue composition assessment by neural network classifier. Percentage of correctly classified images was used as measure of network's performance. Neural network based on corrected images performed better than that based on uncorrected images. ${blacksquare}$ = thickness-corrected images; ${blacktriangleup}$ = original images. Graphs illustrate results from training (A) and testing (B) sets.

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