Mammogram segmentation using multi-atlas deformable registration
Mammogram segmentation using multi-atlas deformable registration
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DOI：
10.1016/j.compbiomed.2019.06.001
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发表时间：
2019-07-01
影响因子：
7.7
通讯作者：
Mukhopadhyay, Sudipta
中科院分区：
文献类型：
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作者：
Sharma, Manish Kumar;Jas, Mainak;Mukhopadhyay, Sudipta
准确的乳房区域分割是各种自动化算法的重要步骤，包括肿块和微钙化等病变的检测，以及有效的远程乳房摄影。虽然传统的分割算法由于图像质量和乳房区域形状的变化而表现不佳，但机器学习的新方法并不容易应用，因为它们需要具有分割图像的大型训练数据集。在本文中，我们提出通过将聚类与可变形图像配准相结合来克服这些限制。使用聚类，我们首先确定了一组最能捕捉乳房x线照片变化的地图集图像。这是使用聚类算法完成的，其中聚类的数量是通过对图像的低维投影进行模型选择来确定的。然后，我们使用可变形图像配准算法将这些地图集图像转移到相似的图像上。我们的技术也克服了在乳房图像中很少有标记用于配准的限制。我们在mini-MIAS和DDSM数据集上对三种现有的最先进的算法进行了评估，使用两个性能指标，Jaccard指数和Hausdorff距离。我们证明，所提出的方法确实能够识别数据集中不同类型的乳房x光片并准确地分割它们。
Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. While traditional segmentation algorithms underperform due to variations in image quality and shape of the breast region, newer methods from machine learning cannot be readily applied as they need a large training dataset with segmented images. In this paper, we propose to overcome these limitations by combining clustering with deformable image registration. Using clustering, we first identify a set of atlas images that best capture the variation in mammograms. This is done using a clustering algorithm where the number of clusters is determined using model selection on a low dimensional projection of the images. Then, we use these atlas images to transfer the segmentation to similar images using deformable image registration algorithm. Our technique also overcomes the limitation of very few landmarks for registration in breast images. We evaluated our method on the mini-MIAS and DDSM datasets against three existing state-of-the-art algorithms using two performance metrics, Jaccard Index and Hausdorff Distance. We demonstrate that the proposed approach is indeed capable of identifying different types of mammograms in the dataset and segmenting them accurately.