## 6 Discussion and Conclusions  
The main objective of this work was to offer a detailed analysis of well-established classical unsupervised segmentation techniques by carefully comparing them in a real clinical application. Breast cancer is the most common cause of cancer death in women worldwide [52; 53] and the second most common cancer overall [54]. Fortunately, science evolution has led to the development of medical imaging techniques, which are used to detect abnormalities in breast parenchyma. Among imaging techniques, multiparametric MRI plays a crucial role and it is widely used in clinical applications, due to its high resolution images and the ability to precisely differentiate soft tissues. As matter of fact, as a case study, the contrast-enhancing mass delineation in breast DCE-MRI was addressed by means of four popular unsupervised segmentation methods, namely: SMRG, k-means, FCM, and sFCM. Although they represent well-known approaches in the literature, they are still widely used in clinical tools. Starting from the basic versions of these approaches, during the initial analysis, we identified the shortcomings of each of them, developing and implementing improved versions, when possible. Nowadays, deep learning techniques represent the state-of-the-art, allowing us to achieve high performance and accurate lesion segmentation for datasets of thousands of patients [14]. Deep learning approaches for image segmentation are generally supervised techniques that require a considerable computation times and a large amount of data for training [15]. In fact, these data must be representative of all the possible scenarios in which the deep neural network could operate, not always available in small- or medium-sized hospitals, and therefore not clinically feasible. Moreover, it should be noted that the authors of [18] showed that semi-automatic approached based on classic unsupervised techniques obtained results comparable or superior to the deep CNNs (namely, SegNet and U-Net). Therefore, this study was focused on classic pattern recognition approaches with the goal of providing an in-depth analysis. It is important to point out that, even considering all the disadvantageous aspects related to manual segmentation, the contribution of an expert radiologist still remains essential at least to validate the results obtained by means a computer-assisted approach. In fact, clinicians rely on computational approaches with interpretable results [41]. From this point of view, the classical unsupervised approaches--such as those analyzed in this  
Figure 15: Case with an irregular lesion: (**a**) entire axial DCE-MRI slice; (**b**) crop of the slice shown in (**a**). Comparison between manual ground-truth (green) and automated segmentation (red). work--provide this important advantage. This aspect is even more critical in deep learning architectures, where CNNs are generally adopted as 'black-box', thus making it difficult to offer a physical interpretation to the features encoded in all the intermediate CNN layers [46]. The obtained experimental results, in terms of area- and distance-based metrics, encourage the use of unsupervised pattern recognition techniques in medical image segmentation. In particular, consistently with [55], clustering-based segmentation approaches achieved better performance compared to the SMRG, the only 'thresholding-based' approach considered. As a consequence, crisp segmentation techniques--such as k-means and SMRG--are not well-suited for medical images that are characterized by an uncertain/variability (sometimes related to the noise), yielding inaccurate boundaries and not well-defined details. Both FCM and sFCM clustering techniques--implementing fuzzy modeling that provides an intrinsic flexibility--significantly achieved the best performance.