In this paper, we propose four variants of the Markov random field model by using constrained clustering for breast mass segmentation. These variants were tested with a set of images extracted from a public database. The obtained results have shown that the proposed variants, which allow to include additional information in the form of constraints to the clustering process, present better visual segmentation results than the original model, as well as a lower final energy which implies a better quality in the final segmentation. Specifically, the centroid initialization method used by our variants allows us to locate about 90% of the regions of interest that contain a mass, which subsequently with the pairwise constraints helped us recover a maximum of 93% of the masses. The segmentation results are also quantitatively evaluated using three supervised segmentation measures. These measures show that the mass segmentation quality of the proposed variants, considering the breast density level, is consistent with the corresponding segmentation annotated by specialized radiologists.

在本文中，我们通过使用约束聚类进行乳房质量分割，提出了马尔可夫随机场模型的四个变体。使用从公共数据库中提取的一组图像对这些变体进行了测试。获得的结果表明，所提出的变体允许以聚类过程的约束形式包括其他信息，与原始模型相比，呈现出更好的视觉分割结果，并且最终能量更低，这意味着模型的质量更高。最后的细分。具体来说，我们的变体使用的质心初始化方法使我们能够定位大约90％的包含质量的感兴趣区域，随后借助成对约束，我们最多可以恢复93％的质量。分割结果还使用三种监督分割方法进行了定量评估。这些措施表明，考虑到乳腺密度水平，提出的变体的质量分割质量与专业放射科医生注释的相应分割一致。