Soft computing approaches for image segmentation: a survey
Image segmentation is the method of partitioning an image into a group of pixels that are
homogenous in some manner. The homogeneity dependents on some attributes like …
homogenous in some manner. The homogeneity dependents on some attributes like …
Automated brain tumour segmentation techniques—a review
M Angulakshmi… - International Journal of …, 2017 - Wiley Online Library
Automatic segmentation of brain tumour is the process of separating abnormal tissues from
normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) …
normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) …
Knowledge-based energy investments of European economies and policy recommendations for sustainable development
This study evaluates the knowledge-based energy investments of European economies. For
this purpose, a novel hybrid fuzzy decision-making model is generated to define important …
this purpose, a novel hybrid fuzzy decision-making model is generated to define important …
Fuzzy C-means clustering through SSIM and patch for image segmentation
In this study, we propose a new robust Fuzzy C-Means (FCM) algorithm for image
segmentation called the patch-based fuzzy local similarity c-means (PFLSCM). First of all …
segmentation called the patch-based fuzzy local similarity c-means (PFLSCM). First of all …
Image segmentation using computational intelligence techniques
Image segmentation methodology is a part of nearly all computer schemes as a pre-
processing phase to excerpt more meaningful and useful information for analysing the …
processing phase to excerpt more meaningful and useful information for analysing the …
An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction
C Luo, C Tan, X Wang, Y Zheng - Applied Soft Computing, 2019 - Elsevier
The prediction of time series has both the theoretical value and practical significance in
reality. However, since the high nonlinear and noises in the time series, it is still an open …
reality. However, since the high nonlinear and noises in the time series, it is still an open …
Ambiguous D-means fusion clustering algorithm based on ambiguous set theory: Special application in clustering of CT scan images of COVID-19
P Singh, SS Bose - Knowledge-Based Systems, 2021 - Elsevier
Abstract Coronavirus Disease 2019 (COVID-19) has been considered one of the most
critical diseases of the 21st century. Only early detection can aid in the prevention of …
critical diseases of the 21st century. Only early detection can aid in the prevention of …
A Novel Type-2 Fuzzy C-Means Clustering for Brain MR Image Segmentation
The fuzzy C-means (FCM) clustering procedure is an unsupervised form of grouping the
homogenous pixels of an image in the feature space into clusters. A brain magnetic …
homogenous pixels of an image in the feature space into clusters. A brain magnetic …
Unsupervised brain tumor segmentation using a symmetric-driven adversarial network
The aim of this study was to computationally model, in an unsupervised manner, a manifold
of symmetry variations in normal brains, such that the learned manifold can be used to …
of symmetry variations in normal brains, such that the learned manifold can be used to …
Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation
Q Wang, X Wang, C Fang, W Yang - Applied Soft Computing, 2020 - Elsevier
The fuzzy C-means (FCM) clustering method is proven to be an efficient method to segment
images. However, the FCM method is not robustness and less accurate for noise images. In …
images. However, the FCM method is not robustness and less accurate for noise images. In …