Magnetic resonance imaging (MRI) is extensively applied in clinical practice. Segmentation of the MRI brain image is significant to the detection of brain abnormalities. However, owing to the coexistence of intensity inhomogeneity and noise, dividing the MRI brain image into different clusters precisely has become an arduous task. In this paper, an improved possibilistic fuzzy ${c}$ -means (FCM) method based on a similarity measure is proposed to improve the segmentation performance for MRI brain images. By introducing the new similarity measure, the proposed method is more effective for clustering the data with nonspherical distribution. Besides that, the new similarity measure could alleviate the “cluster-size sensitivity” problem that most FCM-based methods suffer from. Simultaneously, the proposed method could preserve image details as well as suppress image noises via the use of local label information. Experiments conducted on both synthetic and clinical images show that the proposed method is very effective, providing mitigation to the cluster-size sensitivity problem, resistance to noisy images, and applicability to data with more complex distribution.

中文翻译：

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基于相似度的可能性FCM与标签信息的脑MRI分割

磁共振成像（MRI）在临床实践中得到广泛应用。MRI脑图像的分割对检测脑部异常非常重要。然而，由于强度不均匀性和噪声的共存，精确地将MRI脑图像划分为不同的簇已成为一项艰巨的任务。本文提出了一种基于相似度度量的改进的模糊$ {c} $-均值（FCM）方法，以提高MRI脑图像的分割性能。通过引入新的相似性度量，所提出的方法对于非球形分布的数据聚类更为有效。除此之外，新的相似性度量可以缓解大多数基于FCM的方法所遭受的“集群大小敏感性”问题。同时，所提出的方法可以通过使用局部标签信息来保留图像细节并抑制图像噪声。在合成图像和临床图像上进行的实验表明，该方法非常有效，可以减轻簇大小的敏感性问题，对噪声图像的抵抗力以及对分布更复杂的数据的适用性。