Cupping artifact correction and automated classification for high-resolution dedicated breast CT images Xiaofeng Yang and Shengyong Wu
Cupping artifact correction and automated classification for high-resolution dedicated breast CT images Xiaofeng Yang and Shengyong Wu
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DOI：
10.1118/1.4754654
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发表时间：
2012-10-01
期刊：
影响因子：
3.8
通讯作者：
Fei, Baowei
中科院分区：
文献类型：
--
作者：
Yang, Xiaofeng;Wu, Shengyong;Fei, Baowei
目的：为了开发和测试一种自动算法来分类不同的组织存在于专用的乳腺CT images.Methods：原始CT图像首先校正，以克服杯状伪影，然后使用多尺度双边滤波器，以减少噪声，同时保持边缘信息的图像。由于皮肤和腺体组织在乳腺CT图像上具有相似的CT值，因此使用形态学处理基于其位置信息来识别皮肤掩模。一个改进的模糊C-均值（FCM）分类方法，然后用于分类脂肪和腺体组织的乳腺组织。通过将皮肤掩模的结果与FCM相结合，乳房组织被分类为皮肤、脂肪和腺体组织。为了评估作者的分类方法，作者使用骰子重叠率比较自动分类的结果，通过手动分割8例patient images.Results获得的结果：校正方法能够纠正杯状伪影，提高乳腺CT图像的质量。对于腺体组织，作者的自动分类和手动分割之间的重叠率为91.6% ± 2.0%。结论：杯状伪影校正方法和自动分类方法适用于高分辨率专用乳腺CT图像，并进行了评价。乳房组织分类可以提供关于乳房组成、密度和组织分布的定量测量。(c)2012年美国医学物理学家协会。[http：//dx.doi.org/10.1118/1.4754654]
Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images.Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images.Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% +/- 2.0%.Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. (c) 2012 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4754654]