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Unsupervised Detection of Mammogram Regions of Interest

机译:乳房X光检查区域的无监督检测

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摘要

We present an unsupervised method for fully automatic detection of regions of interest containing fibroglandular tissue in digital screening mammography. The unsupervised segmenter is based on a combination of several unsupervised segmentation results, each in different resolution, using the sum rule. The mammogram tissue textures are locally represented by four causal monospectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous mammogram segments is reached. The performance of the presented method is extensively tested on the Digital Database for Screening Mammography (DDSM) from the University of South Florida as well as on the Prague Texture Segmentation Benchmark using the commonest segmentation criteria and where it compares favourably with several alternative texture segmentation methods.
机译:我们提出了一种无监督的方法,用于在数字化乳腺X线摄影术中全自动检测包含纤维腺组织的目标区域。非监督分割器是基于多个无监督分割结果的组合,每个分割结果具有不同的分辨率,并使用求和规则。乳房X射线照片组织纹理由对每个像素递归评估的四个因果单谱随机场模型局部表示。该算法的单分辨率分割部分基于基础的高斯混合模型,并从过度分割的初始估计开始,该初始估计会进行自适应修改,直到达到均匀的乳房X线照片片段的最佳数量。所提出方法的性能已在南佛罗里达大学的数字化乳腺筛查数字数据库(DDSM)上以及在布拉格纹理分割基准中使用最常见的分割标准进行了广泛的测试,并且与几种替代的纹理分割方法相比具有优势。

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