Mammography segmentation with maximum likelihood active contours
Mammography segmentation with maximum likelihood active contours
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DOI：
10.1016/j.media.2012.05.005
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发表时间：
2012-08-01
影响因子：
10.9
通讯作者：
Hamarneh, Ghassan
中科院分区：
文献类型：
--
作者：
Rahmati, Peyman;Adler, Andy;Hamarneh, Ghassan
我们提出了一种计算机辅助的方法来分割可疑病变的数字乳腺X线照片，基于一种新的最大似然活动轮廓模型，使用水平集（MLACMLS）。该算法估计分割轮廓，最好地分离病变的背景使用伽马分布模型的强度的两个区域（前景和背景）。利用该算法对Gamma分布参数进行了估计。我们评估了MLACMLS在真实的乳腺摄影图像上的性能。我们的结果进行了比较，两个领先的相关方法：自适应水平集为基础的分割方法（ALSSM）和毛刺分割使用水平集（SSLS）的方法，并显示更高的分割精度（MLACMLS：86.85%比ALSSM：74.32%和SSLS：57.11%）。此外，我们的结果进行了定性的比较，主动轮廓无边缘（ACWOE），并显示出更好的性能。此外，使用ML作为目标函数，而不是KL发散和ACWOE的能量泛函的适用性也被证明。我们的算法也被证明是强大的选择所需的单个种子点。(c)2012爱思唯尔有限公司版权所有。
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. (c) 2012 Elsevier B.V. All rights reserved.