Abstract

Detecting automatically overlapping structures is a major issue in segmentation. In addition, the assessment of the quality of the clusters produced by fuzzy segmentation algorithms is one of the challenging tasks in segmentation process. To address this issue, a wide variety of functions called validity indexes have been proposed and developed. The overlap phenomenon constitute a source of failure for most of these indexes, in this context we propose a new cluster validity index V_ECS to recognize the optimal number of clusters adapted to the fuzzy c-means algorithm; based on the entropy, on the partition coefficient functions and on two criteria: the compactness within the classes and the inter-classes separation. We conducted experiments on medical imaging data sets, including simulated brain magnetic resonance imaging (MRI) and leukemia images. The experimental results reveal that, our V_ECS validity index outperform the other existing methods and has greater ability to identify the appropriate number of segments on high overlapped data sets.

中文翻译：

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医学图像重叠聚类中的新有效性指标

摘要

检测自动重叠的结构是分割中的主要问题。另外，对模糊分割算法产生的聚类质量进行评估是分割过程中的一项艰巨任务。为了解决这个问题，已经提出并开发了称为有效性索引的各种各样的功能。重叠现象构成了大多数这些指标的失败根源，在此背景下，我们提出了一个新的聚类有效性指标V _ECS识别适合于模糊c均值算法的最佳聚类数目；基于熵，分配系数函数和两个标准：类内的紧密度和类间分离。我们对医学成像数据集进行了实验，包括模拟的脑磁共振成像（MRI）和白血病图像。实验结果表明，我们的V _ECS有效性指数优于其他现有方法，并且具有较高的能力来识别高重叠数据集上的适当段数。