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Density peaks clustering (DPC) algorithm is able to get a satisfactory result with the help of artificial selecting the clustering centers, but such selection can be hard for a large amount of clustering tasks or the data set with a complex decision diagram. The purpose of this paper is to propose an automatic clustering approach without human intervention. Inspired by the visual selection rule of DPC, the judgment index which equals the lower value within density and distance (after normalization) is proposed for selecting the clustering centers. The judgment index approximately follows the generalized extreme value (GEV) distribution, and each clustering center’s judgment index is much higher. Hence, it is reasonable that the points are selected as clustering centers if their judgment indices are larger than the upper quantile of GEV. This proposed method is called density peaks clustering based on generalized extreme value distribution (DPC-GEV). Furthermore, taking the computational complexity into account, an alternative method based on density peak detection using Chebyshev inequality (DPC-CI) is also given. Experiments on both synthetic and real-world data sets show that DPC-GEV and DPC-CI can achieve the same accuracy as DPC on most data sets but consume much less time. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms have been used in a wide range of applications in clinical aided diagnosis, such as automatic MR image segmentation and seizure EEG signal analyses. In recent years, many machine learning-based automatic MR brain image segmentation methods have been proposed as auxiliary methods of medical image analysis in clinical treatment. Nevertheless, many problems regarding precise medical images, which cannot be effectively utilized to improve partition performance, remain to be solved. Due to the poor contrast in grayscale images, the ambiguity and complexity of MR images, and individual variability, the performance of classic algorithms in medical image segmentation still needs improvement. In this paper, we introduce a distributed multitask fuzzy c-means (MT-FCM) clustering algorithm for MR brain image segmentation that can extract knowledge common among different clustering tasks. The proposed distributed MT-FCM algorithm can effectively exploit information common among different but related MR brain image segmentation tasks and can avoid the negative effects caused by noisy data that exist in some MR images. Experimental results on clinical MR brain images demonstrate that the distributed MT-FCM method demonstrates more desirable performance than the classic signal task method. [ABSTRACT FROM AUTHOR]