Entropy based segmentation of tumor from brain MR images–a study with teaching learning based optimization
V Rajinikanth, SC Satapathy, SL Fernandes… - Pattern Recognition …, 2017 - Elsevier
Image processing plays an important role in various medical applications to support the
computerized disease examination. Brain tumor, such as glioma is one of the life threatening …
computerized disease examination. Brain tumor, such as glioma is one of the life threatening …
Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review
Objectives Medical image analysis practices face challenges that can potentially be
addressed with algorithm-based segmentation tools. In this study, we map the field of …
addressed with algorithm-based segmentation tools. In this study, we map the field of …
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 …
A new approach for brain tumor diagnosis system: single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network
Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks
to these images, some methods have so far been proposed in order to distinguish between …
to these images, some methods have so far been proposed in order to distinguish between …
A framework for susceptibility analysis of brain tumours based on uncertain analytical cum algorithmic modeling
Susceptibility analysis is an intelligent technique that not only assists decision makers in
assessing the suspected severity of any sort of brain tumour in a patient but also helps them …
assessing the suspected severity of any sort of brain tumour in a patient but also helps them …
Modified total Bregman divergence driven picture fuzzy clustering with local information for brain MRI image segmentation
H Lohit, D Kumar - Applied Soft Computing, 2023 - Elsevier
This research work discusses a noise-robust picture fuzzy clustering method with an
application to the MRI image segmentation problem. The MRI images suffer from the …
application to the MRI image segmentation problem. The MRI images suffer from the …
Brain tumor segmentation using neutrosophic expert maximum fuzzy-sure entropy and other approaches
Glioblastoma is the most aggressive and most common primary brain tumor in adult
individuals. Magnetic resonance imagery (MRI) is widely used in the brain tumor diagnosis …
individuals. Magnetic resonance imagery (MRI) is widely used in the brain tumor diagnosis …
Theoretical analysis of picture fuzzy clustering: Convergence and property
PTM Phuong, PH Thong - Journal of Computer Science and Cybernetics, 2018 - vjs.ac.vn
Recently, picture fuzzy clustering (FC-PFS) has been introduced as a new computational
intelligence tool for various problems in knowledge discovery and pattern recognition …
intelligence tool for various problems in knowledge discovery and pattern recognition …
An integrated design of fuzzy C-means and NCA-based multi-properties feature reduction for brain tumor recognition
In medical imaging, brain tumor detection and recognition from magnetic resonance imaging
examination are essential for both the analysis and processing of brain cancers. From the …
examination are essential for both the analysis and processing of brain cancers. From the …
A new multi-criteria decision making algorithm for medical diagnosis and classification problems using divergence measure of picture fuzzy sets
A divergence measure plays an important part in distinguishing two probability distributions
and drawing conclusions based on that discrimination. In this paper, we proposed the …
and drawing conclusions based on that discrimination. In this paper, we proposed the …