Pike Robert, Sechopoulos Ioannis, Fei Baowei
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329.
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 and Winship Cancer Institute of Emory University, Atlanta, Georgia 30322.
Med Phys. 2015 Nov;42(11):6190-202. doi: 10.1118/1.4931958.
To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images.
Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors' classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images.
Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively.
A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.
开发并测试一种自动算法，用于对专用乳腺CT图像中的不同类型组织进行分类。
使用专用乳腺CT临床原型采集了五名不同患者单乳的图像。乳腺CT图像通过多尺度双边滤波器进行处理，以减少噪声同时保留边缘信息，并进行校正以克服杯状伪影。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。训练支持向量机（SVM），并使用所得模型创建脂肪和腺体组织的逐像素分类图。通过将皮肤掩码的结果与SVM结果相结合，将乳腺组织分类为皮肤、脂肪和腺体组织。然后使用该图来识别最小生成森林的标记，该森林利用空间和强度信息生长以分割图像。为了评估作者的分类方法，他们使用DICE重叠率将自动分类的结果与在五名患者图像上通过手动分割获得的结果进行比较。
自动分割与手动分割的比较表明，基于最小生成森林的分类方法能够成功地对专用乳腺CT图像进行分类，脂肪、腺体和皮肤组织的平均DICE比率分别为96.9%、89.8%和89.5%。
提出并评估了一种基于二维最小生成森林的分类方法，用于对专用乳腺CT图像中的脂肪、皮肤和腺体组织进行分类。该分类方法可用于致密乳腺组织定量、辐射剂量评估以及乳腺成像中的其他应用。
