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Three-dimensional active contour model for characterization of solid breast masses on three-dimensional ultrasound images

机译:三维超声肿块特征三维活动轮廓模型

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The accuracy of discrimination between malignant and benign solid breast masses on ultrasound images may be improved by using computer-aided diagnosis and 3-D information. The purpose of this study was to develop automated 3-D segmentation and classification methods for 3-D ultrasound images, and to compare the classification accuracy based on 2-D and 3-D segmentation techniques. The 3-D volumes were recorded by translating the transducer across the lesion in the z-direction while conventional 2-D images were acquired in the x-y plane. 2-D and 3-D segmentation methods based on active contour models were developed to delineate the mass boundaries. Features were automatically extracted based on the segmented mass shapes, and were merged into a malignancy score using a linear classifier. 3-D volumes containing biopsy-proven solid breast masses were collected from 102 patients (44 benign and 58 malignant). A leave-one-out method was used for feature selection and classifier design. The area A_z under the test receiver operating characteristic curves for the classifiers using the 3-D and 2-D active contour boundaries were 0.88 and 0.84, respectively. More than 45% of the benign masses could be correctly identified using the 3-D features without missing a malignancy. Our results indicate that an accurate computer classifier can be designed for differentiation of malignant and benign solid breast masses on 3-D sonograms.
机译:通过使用计算机辅助诊断和3-D信息,可以提高对超声图像上的恶性和良性固体乳房肿块之间的辨别的准确性。本研究的目的是为三维超声图像开发自动化的3-D分段和分类方法,并基于2-D和3-D分段技术进行比较分类精度。通过在Z方向上将换能器转换在Z方向上,而在X-Y平面中获取常规的2-D图像来记录3-D体积。开发了基于主动轮廓模型的2-D和3-D分段方法来描绘群众边界。基于分段质量形状自动提取特征,并使用线性分类器合并为恶性分数。从102名患者中收集了含有活组织检查验证的固体乳腺菌素的3-D体积(44个良性和58个恶性)。休假方法用于特征选择和分类器设计。使用3-D和2-D活动轮廓边界的分类器的测试接收器的区域A_Z分别为0.88和0.84。可以使用3-D功能正确识别超过45%的良性群体,而不会遗漏恶性肿瘤。我们的结果表明,精确的计算机分类器可以设计用于在3-D超声图上的恶性和良性固体乳腺菌群的分化。

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