Robust methods for precise segmentation of breast region or volume from breast X-ray images, including mammogram and tomosynthetic image, is crucial for applications of these medical images. However, this task is challenging because the acquired images not only are inherent noisy and inhomogeneous, but there are also connected or overlapped artifacts, or noises on the images as well, due to loc...

A fuzzy clustering based modification of Gaussian mixture models (GMMs) for speaker recognition is proposed. In this modification, fuzzy mixture weights are introduced by redefining the distances used in the fuzzy c-means (FCM) functionals. Their reestimation formulas are proved by minimising the FCM functionals. The experimental results show that the fuzzy GMMs can be used in speaker recogniti...

Fuzzy c-means (FCM) is a generalization of the classical k-means clustering algorithm to case where an observation can belong several clusters at same time. The was previously observed have initialization problems when number desired or dimensions data are high. We tested FCM against with functional data, generated from stationary Gaussian processes, and thus in principle infinite-dimensional. ...

Clustering is a predominant technique used in image segmentation due to its simple, easy and efficient approach. It very important for the analysis, extraction interpretation of images; which makes it multiple applications various fields. In this article, we propose different based on cooperation between an optimization algorithm Cuckoo Search Algorithm (CSA) clustering Fuzzy C-means (FCM). The...

In this paper, a clustering algorithm named KHarmonic means (KHM) was employed in the training of Radial Basis Function Networks (RBFNs). KHM organized the data in clusters and determined the centres of the basis function. The popular clustering algorithms, namely K-means (KM) and Fuzzy c-means (FCM), are highly dependent on the initial identification of elements that represent the cluster well...

Clustering is the process of grouping data objects into set of disjointed classes called clusters so that objects within a class are highly similar to one another and dissimilar to the objects in other classes. K-means (KM) and Fuzzy c-means (FCM) algorithms are popular and powerful methods for cluster analysis. However, the KM and FCM algorithms have considerable trouble in a noisy environment...

Aiming at partitioning an image into homogeneous and meaningful regions, automatic image segmentation is a fundamental but challenging problem in computer vision. It is well known that Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the FCM-based image segmentation algorithm must be manually estimated to determine cluster number by users. In thi...