UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation
A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery.
Brain tumor detection can be done through magnetic resonance imaging (MRI). Image …
Brain tumor detection can be done through magnetic resonance imaging (MRI). Image …
The variational approximation for Bayesian inference
The influence of this Thomas Bayes' work was immense. It was from here that" Bayesian"
ideas first spread through the mathematical world, as Bayes's own article was ignored until …
ideas first spread through the mathematical world, as Bayes's own article was ignored until …
Automatic visual detection system of railway surface defects with curvature filter and improved Gaussian mixture model
Rails are among the most important components of railway transportation, and real-time
defects detection of the railway is an important and challenging task because of intensity …
defects detection of the railway is an important and challenging task because of intensity …
A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation
SP Chatzis, TA Varvarigou - IEEE Transactions on Fuzzy …, 2008 - ieeexplore.ieee.org
Hidden Markov random field (HMRF) models have been widely used for image
segmentation, as they appear naturally in problems where a spatially constrained clustering …
segmentation, as they appear naturally in problems where a spatially constrained clustering …
Fast and robust spatially constrained Gaussian mixture model for image segmentation
In this paper, a new mixture model for image segmentation is presented. We propose a new
way to incorporate spatial information between neighboring pixels into the Gaussian mixture …
way to incorporate spatial information between neighboring pixels into the Gaussian mixture …
Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel‐Based Fuzzy C‐Means Clustering
An adaptively regularized kernel‐based fuzzy C‐means clustering framework is proposed
for segmentation of brain magnetic resonance images. The framework can be in the form of …
for segmentation of brain magnetic resonance images. The framework can be in the form of …
DM-RIS: Deep multimodel rail inspection system with improved MRF-GMM and CNN
Rail inspection system (RIS) remains an emergent instrumentation for railway transportation,
with its capacity of measuring surface defect on steel rail. However, detecting technique and …
with its capacity of measuring surface defect on steel rail. However, detecting technique and …
Survey of contemporary trends in color image segmentation
SR Vantaram, E Saber - Journal of Electronic Imaging, 2012 - spiedigitallibrary.org
In recent years, the acquisition of image and video information for processing, analysis,
understanding, and exploitation of the underlying content in various applications, ranging …
understanding, and exploitation of the underlying content in various applications, ranging …
A fast and robust image segmentation using FCM with spatial information
XY Wang, J Bu - Digital Signal Processing, 2010 - Elsevier
Automated segmentation of images has been considered an important intermediate
processing task to extract semantic meaning from pixels. In general, the fuzzy c-means …
processing task to extract semantic meaning from pixels. In general, the fuzzy c-means …
Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation
Objective Accurate brain tissue segmentation from magnetic resonance (MR) images is an
essential step in quantitative brain image analysis, and hence has attracted extensive …
essential step in quantitative brain image analysis, and hence has attracted extensive …