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首页> 外文期刊>Medical Physics >Improved dynamic-programming-based algorithms for segmentation of masses in mammograms.
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Improved dynamic-programming-based algorithms for segmentation of masses in mammograms.

机译：改进的基于动态编程的乳腺X线照片质量分割算法。

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In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID2PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID2PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions.
机译：在本文中，提出了两种用于乳腺肿块分割的新的边界跟踪算法。这些新算法基于在Timp和Karssemeijer中提出的基于动态编程的边界跟踪（DPBT）算法。 Timp和N.Karssemeijer，医学博士。物理31，958-971（2004）] DPBT算法包含两个主要步骤：（1）构建局部成本函数，以及（2）将动态规划应用于基于局部成本函数的最佳边界的选择。本文使用一组349幅乳腺摄影图像测试了DPBT算法设计中使用的一些假设的有效性。根据测试结果，设计了对本地成本函数计算的修改，并产生了改进的DPBT（IDPBT）算法。提出了动态选择局部成本函数各组成部分的强度的过程，该过程使这些参数独立于图像数据集。这种动态选择程序的结合产生了另一种新算法，我们称为ID2PBT。还介绍了确定原始论文未涵盖的DPBT算法的其他一些参数的方法。通过与DPBT算法进行比较，实验性地证明了新的IDPBT和ID2PBT算法的优点。根据区域重叠量度和其他分割指标评估分割结果。两种新算法均优于原始DPBT。在与最高分割精度相对应的分割指标值附近，算法性能的提高更为明显，即新算法产生的分割区域更优化，而不是所有分割区域的平均质量都有明显提高。

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