Clustering is one of the most vital image segmentation techniques. However, proper image
clustering has always been a challenging task due to blurred and vague areas near to …

Nature-inspired optimization algorithms are simple and effective tools for solving highly non-
linear and multi-modal real-world problems. Nowadays this kind of methods is very attractive …

Superpixel become increasingly popular in image segmentation field as it greatly helps
image segmentation techniques to segment the region of interest accurately in noisy …

Grouping problems are a special type of combinatorial optimization problems that have
gained great relevance because of their numerous real-world applications. The solution …

Automated medical imagining is growing rapidly for advanced clinical treatment and
intervention in medical diagnosis. The segmentation of nuclei in digital histopathology is …

Partitional clustering-based image segmentation is one of the most significant approaches.
K-means is the conventional clustering techniques even though very sensitive to noise and …

Pathological color image segmentation is an exigent procedure due to the existence of
imperceptibly correlated, and indistinct multiple regions of concern. Multi-level thresholding …

Partitional clustering techniques such as K-Means (KM), Fuzzy C-Means (FCM), and Rough
K-Means (RKM) are very simple and effective techniques for image segmentation. But …

Since the beginning of the twenty-first century, the Cuckoo Search (CS) algorithm has
emerged as one of the robust, flexible, fast, and easily implementable techniques for the …

Fuzzy C-means (FCM) is one of the prominent and effective cluster-based image
segmentation techniques exceedingly susceptible to noise and initial cluster centers …