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Glandular Segmentation of Cone Beam Breast CT Volume Images

机译:锥形束乳腺CT体图像的腺体分割

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Cone beam breast CT (CBBCT) has potential as an alternative to mammography for screening breast cancer while limiting the radiation dose to that of a two-view mammogram. A clinical trial of CBBCT has been underway and volumetric breast images have been obtained. Although these images clearly show the 3D structure of the breast, they are limited by quantum noise due to dose limitations. Noise from these images adds to the challenges of glandular/adipose tissue segmentation. In response to this, an automated method for reducing noise and segmenting glandular tissue in CBBCT images was developed. A histogram based 2-means clustering algorithm was used in conjunction with a seven-point 3D median filter to reduce quantum noise. Following this, a 2D parabolic correction was applied to flatten the adipose tissue in each slice to reduce system inhomogeneities. Finally, a median smoothing algorithm was applied to further reduce noise for optimal segmentation. The algorithm was tested on actual breast scan volume data sets for subjective analysis and on a 3D mathematical phantom to test the algorithm. Subjective comparison of the actual breast scans with the denoised and segmented volumes showed good segmentation with little to no noticeable degradation. The mathematical phantom, after denoising and segmentation, was found to accurately measure the percent glandularity within 0.03% of the actual value for the phantom containing larger spherical shapes, but was only able to preserve small micro-calcification sized spheres of 0.8 and 1.0 mm, and small fibers with diameters of 1.2 and 1.4 mm.
机译:锥形束乳腺CT(CBBCT)可以作为乳腺X线摄影的替代方法,用于筛查乳腺癌,同时将放射线剂量限制为两幅乳房X线照片。 CBBCT的临床试验正在进行中,并且已经获得了乳腺体积图像。尽管这些图像清楚地显示了乳房的3D结构,但由于剂量限制,它们受到量子噪声的限制。这些图像产生的噪声增加了腺体/脂肪组织分割的挑战。响应于此,开发了用于减少噪声和分割CBBCT图像中的腺组织的自动化方法。基于直方图的2均值聚类算法与七点3D中值滤波器结合使用以减少量子噪声。此后,应用2D抛物线校正将每个切片中的脂肪组织弄平,以减少系统不均匀性。最后,应用中值平滑算法进一步降低噪声以获得最佳分割效果。该算法已在实际的乳房扫描量数据集上进行了主观分析,并在3D数学模型上进行了测试。实际乳房扫描与降噪和分割的体积的主观比较表明分割良好,几乎没有降级。经过数学模型的去噪和分割后,发现该模型能够准确地测量包含较大球形的模型的实际值的0.03%以内的腺体百分比,但仅能保留0.8和1.0 mm的微小微钙化球体,以及直径为1.2和1.4毫米的小纤维。

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