Masulli F, Schenone A
Istituto Nazionale per la Fisica della Materia and Dipartimento di Informatica e Scienze dell'Informazio ne, Università di Genova, Italy.
Artif Intell Med. 1999 Jun;16(2):129-47. doi: 10.1016/s0933-3657(98)00069-4.
In medical imaging uncertainty is widely present in data, because of the noise in acquisition and of the partial volume effects originating from the low resolution of sensors. In particular, borders between tissues are not exactly defined and memberships in the boundary regions are intrinsically fuzzy. Therefore, computer assisted unsupervised fuzzy clustering methods turn out to be particularly suitable for handling a decision making process concerning segmentation of multimodal medical images. By using the possibilistic c-means algorithm as a refinement of a neural network based clustering algorithm named capture effect neural network, we developed the possibilistic neuro fuzzy c-means algorithm (PNFCM). In this paper the PNFCM has been applied to two different multimodal data sets and the results have been compared to those obtained by using the classical fuzzy c-means algorithm. Furthermore, a discussion is presented about the role of fuzzy clustering as a support to diagnosis in medical imaging.
在医学成像中，由于采集过程中的噪声以及传感器低分辨率产生的部分容积效应，数据中广泛存在不确定性。特别是，组织之间的边界没有精确界定，边界区域的隶属度本质上是模糊的。因此，计算机辅助无监督模糊聚类方法被证明特别适合处理多模态医学图像分割的决策过程。通过使用可能性c均值算法对一种名为捕获效应神经网络的基于神经网络的聚类算法进行改进，我们开发了可能性神经模糊c均值算法（PNFCM）。本文将PNFCM应用于两个不同的多模态数据集，并将结果与使用经典模糊c均值算法获得的结果进行了比较。此外，还讨论了模糊聚类在医学成像诊断支持中的作用。
