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An atlas based approach to segmenting MLO view mammograms

机译:基于图集的MLO乳房X线照片分割方法

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A method for segmenting mediolateral oblique view mammograms is presented here. While most existing algorithms rely on histogram information to segment the breast region, we propose to use the shape and profile information to achieve more accurate and reliable segmentation. Here, we suggest a two step procedure consisting of feature based registration followed by Demon's implementation of deformable registration. This paper also addresses the issue of automatically detecting suitable landmarks such as nipple and pectoral muscle edge using existing algorithms. The results were evaluated against contours drawn by a radiologist on 18 mammograms selected randomly from the mini-MIAS dataset. Finally, a quantitative comparison using the criteria of false positive rate and false negative rate show that this algorithm clearly outperforms existing methods.
机译:这里提出了一种分割后外侧斜视X线照片的方法。虽然大多数现有算法都依赖于直方图信息来分割乳房区域,但我们建议使用形状和轮廓信息来实现更准确和可靠的分割。在这里,我们建议两步过程,包括基于特征的配准,然后是Demon对可变形配准的实现。本文还讨论了使用现有算法自动检测合适的界标(例如乳头和胸肌边缘)的问题。根据放射线医师从mini-MIAS数据集中随机选择的18幅乳房X线照片上绘制的轮廓对结果进行了评估。最后,使用误报率和误报率标准进行的定量比较表明,该算法明显优于现有方法。

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