基于纹理的乳腺三维病灶分割自适应聚类算法

D. Boukerroui, O. Basset, A. Baskurt, A. Hernandez, N. Guérin, G. Giménez
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引用次数: 7

摘要

提出了一种用于乳腺肿瘤自动提取的算法。该算法涉及灰度和纹理特征图像的三维自适应k均值聚类。分割问题被表述为一个最大a后验(MAP)估计问题。MAP估计是使用Besag的迭代条件模式算法来实现能量函数的最小化。这个函数有三个组成部分。第一种方法约束区域与数据接近,第二种方法施加空间连续性,第三种方法考虑了各个区域的纹理。这种分割技术在活体乳房数据上得到了验证。结果表明这种方法非常有效。结果与专家手工分割的病灶进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Texture based adaptive clustering algorithm for 3D breast lesion segmentation
A specific algorithm is presented for the automatic extraction of breast tumors. This algorithm involves 3D adaptive K-means clustering of the gray-scale and texture features images. The segmentation problem is formulated as a Maximum A Posterior (MAP) estimation problem. The MAP estimation is achieved using Besag's Iterated Conditional Modes algorithm for the minimization of an energy function. This function has three components. The first one constrains the region to be close to the data, the second imposes spatial continuity and the third takes into consideration the texture of the various regions. This segmentation technique is demonstrated on in vivo breast data. The method revealed very efficient. The results are compared with the manual segmentation of lesions by an expert.
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