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Mammography segmentation with maximum likelihood active contours

机译：具有最大似然活动轮廓的乳腺摄影分割

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摘要

We present a computer-aided approach to segmenting suspicious lesions in digital mammograms, based on a novel maximum likelihood active contour model using level sets (MLACMLS). The algorithm estimates the segmentation contour that best separates the lesion from the background using the Gamma distribution to model the intensity of both regions (foreground and background). The Gamma distribution parameters are estimated by the algorithm. We evaluate the performance of MLACMLS on real mammographic images. Our results are compared to those of two leading related methods: The adaptive level set-based segmentation method (ALSSM) and the spiculation segmentation using level sets (SSLS) approach, and show higher segmentation accuracy (MLACMLS: 86.85% vs. ALSSM: 74.32% and SSLS: 57.11%). Moreover, our results are qualitatively compared with those of the Active Contour Without Edge (ACWOE) and show a better performance. Further, the suitability of using ML as the objective function as opposed to the KL divergence and to the energy functional of the ACWOE is also demonstrated. Our algorithm is also shown to be robust to the selection of a required single seed point.
机译：我们基于使用级别集（MLACMLS）的新型最大似然活动轮廓模型，提出了一种计算机辅助方法，用于对数字化X线照片中的可疑病变进行分割。该算法使用Gamma分布估算两个区域（前景和背景）的强度，从而最好地将病变与背景区分开来的分割轮廓。伽玛分布参数由算法估算。我们评估MLACMLS在真实乳腺摄影图像上的性能。我们的结果与两种领先的相关方法的结果进行了比较：自适应基于水平集的分割方法（ALSSM）和使用水平集的针状分割法（SSLS），显示出更高的分割精度（MLACMLS：86.85％vs. ALSSM：74.32 ％和SSLS：57.11％）。此外，我们的结果与无轮廓主动轮廓（ACWOE）的结果进行了定性比较，并显示出更好的性能。此外，还证明了使用ML作为目标函数而不是KL散度和ACWOE的能量函数的适用性。我们的算法还显示出对选择所需单个种子点的鲁棒性。

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