本文回顾了模糊c均值聚类(FCM)的潜在用途,并探讨了对距离函数和质心初始化方法的修改以增强图像分割。本文中感兴趣的应用是乳房X线照片中乳腺肿瘤的分割。乳腺癌是加拿大女性癌症死亡的第二大原因。早期检测可降低治疗成本,并为患者提供良好的预后。经典方法,比如乳房X线照片,依靠放射科医生来检测癌症肿瘤,这引入了癌症检测中人为错误的可能性。经典方法是劳动密集型的,and,因此,昂贵的医疗资源。最近的研究补充了自动乳房X线照片分析的经典方法。基本的FCM方法依赖于欧几里得距离,这对于测量非球形结构不是最佳的。为了解决这些限制,我们回顾了基于马氏距离的FCM(FCM-M)的实施。本文的三个目标是:(1)审查FCM,FCM-M,和文献中的三种质心初始化算法,(2)说明了这些算法在图像分割中的有效性,和(3)开发一个Python包,使用优化的算法上传到GitHub。对算法的图像分析表明,使用三种质心初始化算法之一可以提高FCM的性能。与基本FCM相比,FCM-M产生了更高的聚类精度,并更好地概述了肿瘤结构。
This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we
review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1)
review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.