Ulrich Bick, MD Maryellen L. Giger PhD Robert A. Schmidt, MD Robert M. Nishikawa, PhD Kunio Doi, PhD 1 報告者：劉治元

ABSTRACT INTRODUCTION DESCRIPTION OF THE ENHANCEMENT ALGORITHM EVALUATION OF THE ALGORITHM ADVANTAGES AND LIMITATIONS OF USING THE ALGORITHM CONCLUSIONS 2

When digital mammograms are viewed on video displays, evaluation of the skin and subcutaneous tissue is often difficult and may require special window settings. An algorithm has been developed for selective enhancement (ie, density correction) of the dark peripheral portions of the breast on mammograms. 3

After enhancement, skin and breast parenchyma can be evaluated simultaneously without the need for different window settings. When tested on a set of 400 digitized mammograms, the density correction algorithm significantly (P <.0001) increased the maximum area of breast tissue visualized simultaneously at window width settings of △ OD (optical density). 4

The algorithm for correcting the density of peripheral breast tissue substantially facilitates and improves the display of digital mammograms and thus will be a valuable component of an integrated workstation for computer-aided diagnosis in mammography. 5

Evaluation of the skin and subcutaneous tissue on mammograms is often difficult because of the high optical density (OD) of these areas. visualization of these peripheral areas on soft-copy displays requires wide window settings, which result in loss of contrast in the center portion of the breast. Special anatomic filters have been designed for x-ray beam equalization in mammography, but they may cause artifacts and have not been accepted in routine clinical practice because of difficulties in handling. 6

Another approach is the use of scanning equalization mammography, which is performed with a modulated, segmented fan beam. Although this technique may correct large differences in regional density in the center part of the breast, it usually cannot uniformly correct the rapid density slope along the semicircular periphery of the breast because of the rectangular design of the fan beam segments. 7

we are developing an automated post-processing algorithm for digital mammograms that selectively enhances the dark peripheral portions of the breast to match the density of the center part of the breast. 8

After an automated segmentation of the digital mammogram and identification of the skin line 9

the distance from the skin is calculated for each pixel inside the breast by using a so-called Euclidean distance map. This map codes the distance from the skin for each image point in the form of a gray value. 10

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a fitted enhancement curve is created; this curve defines the necessary correction value for each breast pixel as a function of the distance from the skin. 12

After application of the enhancement algorithm, skin, subcutaneous tissue, and breast parenchyma have similar average density values and can be evaluated simultaneously without the need for different window settings. 13

The calculation time for the algorithm depends on the size of the breast and ranges between 30 and 50 seconds for a full-size 2,048 x 2,560 image (when performed on a Power station 590 [RISC 6000 series; IBM, Austin, Tex]). More than 50% of this time is spent to create the Euclidean distance map, whereas image segmentation takes only about 2 seconds. 14

Methods The algorithm for correcting peripheral density was tested on the four-view screening mammographic studies of 100 consecutive patients in whom non- palpable breast cancer was diagnosed at the University of Chicago between 1987 and The 400 mammograms were digitized with a laser scanner (Lumiscan 100; Lumisys, Sunnyvale, Calif) at 100 μm with a 10-bit gray scale and a dynamic range of OD. 15

For evaluation, the original and density-corrected images were displayed back-to-back on a computer monitor. Density correction results were subjectively rated by four expert mammographers (R.A.S., R.C. Haldemann, C.J. Vyborny, D.E. Wolverton) into the following five categories. 16

five categories: (a) optimal results, with uniform density along the complete periphery of the breast (b) minor density irregularities, with smooth areas of slightly higher or lower density (c) somewhat distracting density irregularities, but with no interference with interpretation; (d) severe density deviations or artifacts that might interfere with interpretation (e) complete failure of the density correction algorithm. 17

As a quantitative measurement, the maximum fractions of the breast area visualized simultaneously at window width settings of 0.5, 1.0, 1.5, and 2.0 △ OD (difference in ODs between upper and lower window limits) were calculated for both the original and density-corrected images. These OD ranges correspond to 146, 293, 438, and 585 gray levels, respectively, in the digitized image (10-bit linear digitization from 0 to 3.5 OD). 18

For statistical analysis of the differences between the original and density-corrected images, the Wilcoxon signed-ranks test for matched pairs was used. As a measure of the above-chance inter-observer agreement, the weighted k for ordered categorical data was calculated for each pair of observers. 19

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In 82% of cases with a discordant rating between two observers, the disagreement was between categories 1 and 2. This may reflect the difficulties of the observers in consistently distinguishing between cases with minor irregularities (category 2) and cases with optimal results (category 1). 22

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Improved Digital Display One of the main limitations of current video monitors for viewing radiographs and especially mammograms is the small dynamic range compared with that available when radiographic film is viewed on a conventional light box. When the described density correction algorithm is used, a larger portion of the breast can be displayed at a narrow (high-contrast) window setting, thus compensating for the smaller dynamic range of the digital display. 24

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Aid to Computer-assisted Detection In addition to improving the display of digital mammograms, the density correction algorithm is used as part of a new single-image method for computer- assisted detection of mass lesions on mammograms. Several of the feature extraction steps used in characterization of masses seen on mammograms are also gradient based and will benefit from use of a density-corrected image as input. 26

Limitations the curve may not be optimally suited for the entire circumfcrcnce of the breast. The density correction algorithm crucially depends on the quality of the digitization process. 27

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The developed algorithm for correcting the density of peripheral breast tissue substantially facilitates and improves the display of digital mammograms. On density-corrected mammograms, the skin, subcutaneous tissue, and breast parenchyma can be evaluated simultaneously at high contrast with use of the same window setting. This algorithm will thus be a valuable component of an integrated workstation for computer-aided diagnosis in mammography. 30

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