###### Abstract
As a consequence, semi-automatic approaches provide more reproducible measurements--compared to fully-manual ones--with a significant reduction of the segmentation time. In particular, fully-automatic segmentation approaches do not require any user interaction. Most existing fully-automatic approaches exploit machine learning or deep learning techniques--such as Support Vector Machines (SVMs) or deep Convolutional Neural Networks (CNNs)--to successfully handle the variability characterizing biomedical data [14; 15]. In this scenario, supervised learning techniques are, in general, more complex since they require high computation times and a large amount of labeled data for training. Furthermore, it is important to point out that--from a clinical feasibility perspective--machine learning approaches, which do not require training, are advantageous: the amount of labeled data needed to adequately train and evaluate the approaches based on deep learning is not always available [16; 17]. The aim of this work is to show the potential of unsupervised pattern recognition techniques, which do not require training and can be more easily integrated into care routine, especially from a clinical feasibility perspective. For this reason, supervised approaches--such as CNNs--were not treated here. As a relevant case study, we consider the segmentation of contrast-enhancing masses on DCE-MRI. It is worth noting that, in [18], classical unsupervised techniques, both automatic and semi-automatic, allowed us to obtain results comparable or superior to the deep learning approaches. From experimental evidence, it is possible to observe that fuzzy clustering techniques significantly outperformed direct region detection approaches (i.e., split-and-merge and region growing) and crisp k-means. FCM and sFCM obtained comparable results, although the integration of spatial information into the sFCM allowed for the best performance. Therefore, the explicit management of segmentation uncertainty via multiple degrees of class memberships, along with spatial information, represented the best computational framework for the problem at hand. The main contributions of this study are:  
* unsupervised segmentation methods, based on classic pattern recognition techniques, can still provide an effective solution despite the prevalence of supervised approaches, such as deep CNNs;
* the use of traditional unsupervised approaches--requiring no training--is an advantage in terms of of immediate clinical feasibility;
* fuzzy clustering techniques significantly outperformed direct region detection approaches and crisp k-means;
* the integration of spatial information into the FCM algorithm achieved the best performance;with the goal of providing a guide for beginners, as well as possibly enabling new future extensions from other researchers, this study provides all the technical information needed to understand both the functioning of each of the studied algorithms and the implemented workflow. The remainder of this work is organized as follows. Section 2 introduces the theoretical background about unsupervised segmentation approaches, focusing on the algorithms used in this study. A detailed description of the performed analysis and of the implemented processing steps is proposed in Section 3, where the exploited DCE-MRI dataset is also described. Section 4 formulates the metrics used to evaluate the performance of the analyzed approaches. Section 5 illustrates the experimental results, including a discussion about the comparison of the proposed techniques. Finally, discussion and conclusions are provided in Section 6.