FCM与KFCM-Ⅱ算法在医学MRI图像分割中的应用

FCM and KFCM—Ⅱ Algorithm with Application in Medical MRI Image Segmentation

医学图像分割在医学图像处理,尤其是临床诊断的MRI图像分析中起着重要的作用。由于医学成像过程中存在着各种退化因素,当前各种分割算法仍难以很好地满足高层应用的需求。为解决这一问题,FCM(Fuzzy C-means clustering)算法和它的核方法版本KFCM(Kernel-based FCM)可以应用于图像分割以取得更好的性能表现。对FCM和KFCM-Ⅱ算法应用于MRI图像分割进行了比较,讨论了在这两种算法中应用灰度有偏场纠正的效果。实验结果表明,在FCM和KFCM-Ⅱ中采用有偏场模型可以取得更好的分割性能。

Medical image segmentation plays an important role in medical image processing,especially in MRI（Magnetic Resonance Imaging） image analysis for clinical diagnosis.Due to a variety of degradation factors in medical imaging process,the existed segmentation algorithms are still hard to satisfyingly meet the need of higher layer applications.For tackling this problem,FCM（Fuzzy C-means Clustering） and its kernel-based version KFCM（Kernel-based FCM） can be applied to image segmentation and achieve better performance. A comparison of FCM and KFCM--II algorithm with application in MRI image segmentation has been presented, and a discussion on the effect of intensity bias field correction in KFCM--II algorithm is given. The experiments show that better segmentation performance can be achieved by adopting bias field model to FCM and KFCM algorithms.