Fuzzy C-mean (FCM) is an algorithm for data segmentation and classification, robust and
very popular within the scientific community. It is used in several fields such as computer …

Techniques of medical image processing and analysis play a crucial role in many clinical
scenarios, including in diagnosis and treatment planning. However, immense quantities of …

Parallel processing has turned into one of the emerging fields of machine learning due to
providing consistent work by performing several tasks simultaneously, enhancing reliability …

Image segmentation in the medical imagery such as MRI, is an essential step to the
sensitive analysis of human tissues lesions with the objective to improve the partition of …

At present, digital image processing plays a vital role in medical imaging areas and
specifically in magnetic resonance imaging (MRI) of brain images such as axial and coronal …

The aim of this paper is to present a GPU parallel algorithm for brain tumor detection to
estimate its size and location from surface temperature distribution obtained by …

Classification task is a very popular preprocessing step in different research fields. Its main
role is to separate the different components of an object or dataset into homogeneous …

In this paper a meaningful parallel implementation of spatial fuzzy c-means (SFCM) is
presented. It has an advantage of being a powerful tool of classical fuzzy c-means. The great …

The proposed work introduces a modified method of fuzzy c means (FCM) algorithm using
bias field correction and partial supervision techniques. The proposed method is named as …

Image intensity bias correction is still an open problem. Fuzzy c-means based bias
correction methods are popular. However, they are time consuming and the exact clustering …