Type-re duce d vague possibilistic fuzzy clustering for medical images

摘要

Soft computing provides the framework for dealing with the uncertainty and imprecision inherent in real-life applications. Soft computing has become a long-standing notable paradigm for medical image processing. A typical fuzzy clustering uses the fuzzy membership function. Nevertheless, there is an alternative membership representation, known as typicality or possibilistic membership. Unlike fuzzy membership that is probabilistic in nature, typicality represents an absolute membership and it is the degree of belonging of an object to a class that does not depend on its distances from the other classes. However, both fuzzy membership and typicality play important role in assigning membership to an object. This study proposes a novel clustering model that creates a vague environment enriched with the concept of fuzzy membership and typicality, while the use of type-reduction plays an essential role in capturing all the vagueness present in the data set. The proposed model is called type-reduced vague possibilistic fuzzy clustering (TVPFC), and we use MRI images to demonstrate its superior robustness over that of FCM (fuzzy c-means), PCM (possibilistic c-means), VCM (vague c-means) and IPFCM (interval-valued possibilistic fuzzy c-means). (c) 2020 Elsevier Ltd. All rights reserved.