Firefly algorithm in biomedical and health care: advances, issues and challenges
Since the past decades, most of the nature inspired optimization algorithms (NIOA) have
been developed and become admired due to their effectiveness for resolving a variety of …
been developed and become admired due to their effectiveness for resolving a variety of …
Firefly algorithm and its variants in digital image processing: A comprehensive review
The significance and requirements of digital image processing arise from two main areas of
applications: the improvement of visual information for human interpretation and the …
applications: the improvement of visual information for human interpretation and the …
An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation
The artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and
has been shown to be efficient in contrast to other optimization algorithms. Nonetheless …
has been shown to be efficient in contrast to other optimization algorithms. Nonetheless …
[PDF][PDF] A hybrid firefly algorithm with fuzzy-C mean algorithm for MRI brain segmentation
MK Alsmadi - American Journal of Applied Sciences, 2014 - researchgate.net
Image processing is one of the essential tasks to extract suspicious region and robust
features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation …
features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation …
Fuzzy-crow search optimization for medical image segmentation
A Lenin Fred, SN Kumar, P Padmanaban… - Applications of hybrid …, 2020 - Springer
Abstract Fuzzy C Means (FCM) clustering technique is widely used in medical image
segmentation. The classical FCM is sensitive to noise and the objective function often gets …
segmentation. The classical FCM is sensitive to noise and the objective function often gets …
A K-means-galactic swarm optimization-based clustering algorithm with Otsu's entropy for brain tumor detection
Image segmentation is a technique in order to segment an image into various parts and
derive meaningful information out of each one. In this article, problem of image …
derive meaningful information out of each one. In this article, problem of image …
Fuzzy clustering algorithm based on improved global best-guided artificial bee colony with new search probability model for image segmentation
Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been
extensively investigated and successfully implemented in image segmentation. FCM is …
extensively investigated and successfully implemented in image segmentation. FCM is …
Chaotic firefly algorithm-based fuzzy C-means algorithm for segmentation of brain tissues in magnetic resonance images
Image segmentation with clustering approach is widely used in biomedical application.
Accurate brain Magnetic Resonance (MR) image segmentation is a challenging task due to …
Accurate brain Magnetic Resonance (MR) image segmentation is a challenging task due to …
Fully automatic grayscale image segmentation based fuzzy C-means with firefly mate algorithm
Image segmentation is the method of dividing an image into many segments, comprising
groups of pixels. It is a process used to determine objects within the image. Fuzzy c-means …
groups of pixels. It is a process used to determine objects within the image. Fuzzy c-means …
A comprehensive review of the firefly algorithms for data clustering
MKA Ariyaratne, TGI Fernando - Advances in Swarm Intelligence …, 2022 - Springer
Separating a given data set into groups (clusters) based on their natural similar
characteristics is one of the main concerns in data clustering. A cluster can be defined as a …
characteristics is one of the main concerns in data clustering. A cluster can be defined as a …