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Rib Detection for Whole Breast Ultrasound Image

机译:整个乳房超声图像的肋骨检测

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摘要

Recently, the whole breast ultrasound (US) is a new advanced screening technique for detecting breast abnormalities. Because a lot of images are acquired for a case, the computer-aided system is needed to help the physicians to reduce the diagnosis time. In the automatic whole breast US, the ribs are the pivotal landmark just like the pectoral muscle in the mammography. In this paper, we develop an automatic rib detection method for the whole breast ultrasound. The ribs could be helpful to define the screening area of a CAD system to reduce the tumor detection time and could be used to register different passes for a case. In the proposed rib detection system, the whole breast images are subsampled at first in order to reduce the computation of rib detection without reducing the detection performance. Due to the shadowing is occurred under the rib in the whole breast ultrasound images and is the sheet-like structure, the Hessian analysis and sheetness function are adopted to enhance the sheet-like structure. Then, the orientation thresholding is adopted to segment the sheet-like structures. In order to remove the non-rib components in the segmented sheet-like structures, some features of ribs in whole breast ultrasound are used. Thus, the connected component labeling is applied and then some characteristics such as orientation, length and radius are calculated. Finally, some criteria are applied to remove non-rib components. In our experiments, there are 65 ribs in 15 test cases and the 62 ribs have been detected by the proposed system with the detection ratio 95.38%. The ratio of position difference under 5 mm is 87.10 % and the ratio of length difference under 10 mm is 85.48 %. The results show that the proposed system almost could detect the ribs in the breast US images and has a good accuracy.
机译:最近,全乳超声(US)是一种用于检测乳房异常的新的先进筛查技术。因为要获取大量图像,所以需要计算机辅助系统来帮助医生减少诊断时间。在自动全乳US中,就像乳房X光检查中的胸肌一样,肋骨是关键的标志。在本文中,我们开发了一种用于整个乳房超声的自动肋骨检测方法。肋骨可能有助于定义CAD系统的筛查区域,以减少肿瘤检测时间,并可用于记录病例的不同通过次数。在提出的肋骨检测系统中,首先对整个乳房图像进行二次采样,以减少肋骨检测的计算而不会降低检测性能。由于在整个乳腺超声图像中的肋骨下方会出现阴影,并且是片状结构,因此采用了Hessian分析和张性功能来增强片状结构。然后,采用方向阈值分割片状结构。为了去除分段的片状结构中的非肋骨成分,在整个乳房超声中使用了肋骨的某些特征。因此,将应用连接的组件标签,然后计算一些特性,例如方向,长度和半径。最后,应用一些标准来删除非肋骨组件。在我们的实验中,在15个测试案例中有65条肋骨,所提出的系统已经检测到62条肋骨,检出率为95.38%。 5mm以下的位置差的比例为87.10%,10mm以下的长度差的比例为85.48%。结果表明,所提出的系统几乎可以检测出美国乳腺图像中的肋骨,具有良好的准确性。

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