A distinctive approach in brain tumor detection and classification using MRI
J Amin, M Sharif, M Yasmin, SL Fernandes - Pattern Recognition Letters, 2020 - Elsevier
A very exigent task for radiologists is early brain tumor detection. Brain tumor raises very
fast, its average size doubles in just twenty-five days. If not treated properly, the survival rate …
fast, its average size doubles in just twenty-five days. If not treated properly, the survival rate …
[HTML][HTML] Brain tumor segmentation based on a hybrid clustering technique
E Abdel-Maksoud, M Elmogy, R Al-Awadi - Egyptian Informatics Journal, 2015 - Elsevier
Image segmentation refers to the process of partitioning an image into mutually exclusive
regions. It can be considered as the most essential and crucial process for facilitating the …
regions. It can be considered as the most essential and crucial process for facilitating the …
Image restoration: total variation, wavelet frames, and beyond
The variational techniques (eg the total variation based method) are well established and
effective for image restoration, as well as many other applications, while the wavelet frame …
effective for image restoration, as well as many other applications, while the wavelet frame …
A two-stage image segmentation method using a convex variant of the Mumford--Shah model and thresholding
The Mumford--Shah model is one of the most important image segmentation models and
has been studied extensively in the last twenty years. In this paper, we propose a two-stage …
has been studied extensively in the last twenty years. In this paper, we propose a two-stage …
Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN)
M Sharma, GN Purohit, S Mukherjee - Networking Communication and …, 2018 - Springer
Medical imaging plays a significant role in the field of medical science. In present scenario
image segmentation is used to extract abnormal tissues from normal tissues clearly in …
image segmentation is used to extract abnormal tissues from normal tissues clearly in …
An efficient iterative thresholding method for image segmentation
We proposed an efficient iterative thresholding method for multi-phase image segmentation.
The algorithm is based on minimizing piecewise constant Mumford–Shah functional in …
The algorithm is based on minimizing piecewise constant Mumford–Shah functional in …
Sparse representation on graphs by tight wavelet frames and applications
B Dong - Applied and Computational Harmonic Analysis, 2017 - Elsevier
In this paper, we introduce a new (constructive) characterization of tight wavelet frames on
non-flat domains in both continuum setting, ie on manifolds, and discrete setting, ie on …
non-flat domains in both continuum setting, ie on manifolds, and discrete setting, ie on …
Weighted variational model for selective image segmentation with application to medical images
Selective image segmentation is an important topic in medical imaging and real
applications. In this paper, we propose a weighted variational selective image segmentation …
applications. In this paper, we propose a weighted variational selective image segmentation …
Image restoration: Wavelet frame shrinkage, nonlinear evolution pdes, and beyond
In the past few decades, mathematics based approaches have been widely adopted in
various image restoration problems; the partial differential equation (PDE) based approach …
various image restoration problems; the partial differential equation (PDE) based approach …
The iterative convolution–thresholding method (ICTM) for image segmentation
Variational methods, which have been tremendously successful in image segmentation,
work by minimizing a given objective functional. The objective functional usually consists of …
work by minimizing a given objective functional. The objective functional usually consists of …