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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基于相似性测量的可能性 FCM 与标签信息用于脑 MRI 分割


磁共振成像（MRI）广泛应用于临床实践。 MRI 大脑图像的分割对于检测大脑异常具有重要意义。然而，由于强度不均匀性和噪声的共存，将MRI脑图像精确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性度量的改进的可能模糊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，该方法对于非球形分布的数据进行聚类更加有效。除此之外，新的相似性度量可以缓解大多数基于 FCM 的方法所面临的“簇大小敏感性”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。对合成图像和临床图像进行的实验表明，所提出的方法非常有效，可以缓解簇大小敏感性问题，抵抗噪声图像，并且适用于具有更复杂分布的数据。