As a mainstream research direction in the field of image segmentation, medical image
segmentation plays a key role in the quantification of lesions, three-dimensional …

Nowadays, pancreas segmentation in CT scans has gained more and more attention for
computer-assisted diagnosis of inflammation (pancreatitis) or cancer. Despite the thrilling …

Fuzzy clustering has been useful in capturing the uncertainty present in the data during
clustering. Most of the c-Means algorithms such as FCM (Fuzzy c-Means), IFCM …

Fuzzy c-means (FCM) is a well-known unsupervised clustering algorithm based on fuzzy
logic and used in many applications. However, it has some disadvantages. One …

Hierarchical clustering techniques help in building a tree-like structure called dendrogram
from the data points which can be used to find the closest related data objects. This paper …

In this paper, an intuitionistic center-free fuzzy c-means clustering method (ICFFCM) is
proposed for magnetic resonance (MR) brain image segmentation. First, in order to …

In real-world scenario, mostly, the datasets are either imprecise or uncertain in their original
form. Due to this reason, the clustering of such datasets is unsatisfactory and we often get …

Driver stress can lead to traffic deaths and injuries which ultimately bring on world economic
loss. Researchers are in full swing to develop various algorithms for driver stress recognition …

Medical image segmentation is a challenging task owing to its aliasing artifacts, existence of
mixed pixels, interference of noise and blurring effect, etc. The brain and the spinal cord …

There is no doubt that brain tumors are one of the leading causes of death in the world. A
biopsy is considered the most important procedure in cancer diagnosis, but it comes with …