脑磁共振成像(MRI)在临床上得到了大量的应用,准确分割脑组织结构可以提高脑疾病诊断的可靠性和治疗方案的有效性。模糊C-均值聚类(FCM)算法擅长解决图像中存在的模糊性和不确定性问题,是最常用的脑MRI分割方法。但因FCM仅利用图像灰度信息,没有考虑区域信息,导致其抗噪性能很差,常与区域信息结合进行改进。马尔可夫随机场(MRF)算法充分利用了图像区域信息,但容易出现过分割现象,因此FCM常与MRF进行结合改进。针对现有的FCM和MRF结合方式上存在的问题,提出了一种新型的自适应权值的FCM与MRF结合算法,用于脑MR图像分割。该算法利用了图像邻域像素的区域相关性,自适应的更新联合场的权值,改进了现有的权值固定的结合方式,充分发挥了FCM和MRF各自的优势,使二者结合更加合理。实验结果表明,本文算法较FCM和现存的一些FCM改进算法有更强的抗噪声能力和更高的分割精度。

Brain magnetic resonance imaging (MRI) has been widely used in clinical practice.Accurate segmentation of brain tissue structure can improve the reliability of the brain disease diagnosis and the effectiveness of treatments. The fuzzy C-Means Clustering (FCM) algorithm is good at solving ambiguities and uncertainties in images, and it is one of the most common brain MRI segmentations. However, FCM has a poor anti-noise ability, because it only uses the grayscale information without considering regional information. The Markov Random Field (MRF) algorithm takes full advantage of the image regional information, but it tends to over-segment. Therefore, we use FCM often combined with MRF to improve the results. In this paper, considering the problem in the existing combination algorithms of FCM and MRF, we propose a new adaptive weight combination of FCM and MRF algorithm for brain MRI segmentation. The algorithm adaptively updates the combining field weight parameter α, using spatial relativity of the adjacent pixel regions. It improves the existing fixed weight combination methods of FCM and MRF, and makes full use of FCM and MRF. Experiment results show that this algorithm has stronger anti-noise property and higher segmentation precision than FCM and some other FCM improved algorithms.