Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.

Graphical Abstract

Figure. The principle of Euclidean distance weighting for unlabeled samples

中文翻译：

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基于知识加权和聚类中心学习的半监督模糊聚类在乳腺钼目标图像分割中的应用

乳腺癌通常通过乳房X光检查来诊断。利用图像分割算法分离乳腺X线摄影中的病灶区域，可以方便医生诊断，减少工作量，具有重要的临床意义。由于难以获得大型、准确标记的医学图像数据集，传统的聚类算法作为无监督模型广泛应用于医学图像分割中。传统的无监督聚类算法的学习知识有限。而且，一些半监督模糊聚类算法无法充分挖掘标记样本的信息，导致监督不足。当面对复杂的乳腺X线摄影图像时，上述算法无法准确分割病灶区域。为了解决这个问题，提出了一种基于知识加权和聚类中心学习的半监督模糊聚类（WSFCM_V）。根据先验知识，提出了三种学习模式：聚类中心的知识加权方法、未标记样本的欧氏距离权重以及从标记样本集的聚类中心学习。这些策略提高了聚类性能。在真实的乳腺钼目标图像上，将WSFCM_V算法与当前流行的半监督和无监督聚类算法进行了比较。WSFCM_V具有最佳的评价指标值。实验结果表明，与现有的聚类算法相比，无论是对于肿瘤区域等较大的病灶区域，还是对于钙化点区域等较小的病灶区域，WSFCM_V都比其他聚类算法具有更高的分割精度。

图形概要

数字。无标记样本的欧式距离加权原理