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Super-resolved multi-channel fuzzy segmentation of MR brain images

Ying Bai, Xiao Han, Dzung L. Pham, Jerry L. Prince, J. Michael Fitzpatrick, Joseph M. Reinhardt
2005 Medical Imaging 2005: Image Processing  
We propose a new fuzzy segmentation framework that incorporates the idea of super-resolution image reconstruction.  ...  The new framework is designed to segment data sets comprised of orthogonally acquired magnetic resonance (MR) images by taking into account their different system point spread functions.  ...  This yields the following algorithm for super-resolved fuzzy segmentation (the equations for which are derived in Appendix A): 1.  ... 
doi:10.1117/12.595357 dblp:conf/miip/BaiHPP05 fatcat:t6uyspbedfethdvwujpn25zacy

A Hybrid Convolutional Neural Network and Deep Belief Network for Brain Tumor Detection in MR Images

2019 International journal of recent technology and engineering  
First, MR image is pre-processed, segmented and classified utilizing image processing techniques.  ...  Early tumor detection in brain plays vital role in early tumor detection and radiotherapy. MR images are used as the input image for brain tumor finding and classify the type of brain tumor.  ...  The method of dividing an image into sets of pixels which is also called super pixels is the image segmentation. The chief objective of segmentation is to identify the tumor's location.  ... 
doi:10.35940/ijrte.b1193.0782s419 fatcat:wgzytnphlbeqlc5fyfswllkcqi

Super-Resolution Reconstruction of 3T-Like Images From 0.35T MRI Using a Hybrid Attention Residual Network

Jialiang Jiang, Fulang Qi, Huiyu Du, Jianan Xu, Yufu Zhou, Dayong Gao, Bensheng Qiu
2022 IEEE Access  
(3T-like MR images).  ...  Thus, the current state of the image quality indicates the need for further research to improve the image quality of low-field systems.  ...  The SR methods can be categorized based on the number of input LR images to single image super-resolution (SISR) [3] and multi-image super-resolution (MISR) [4] .  ... 
doi:10.1109/access.2022.3155226 fatcat:ulndrxjojzhifdwnl53ziqmy5e

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, Levente Lanczi, Elizabeth Gerstner (+56 others)
2015 IEEE Transactions on Medical Imaging  
GEREMIA, MENZE & AYACHE (2012): SPATIAL DECISION FORESTS FOR GLIOMA SEGMENTATION IN MULTI-CHANNEL MR IMAGES Medical imaging protocols produce large amounts of multimodal volumetric images.  ...  , multi-modality brain segmentation framework.  ... 
doi:10.1109/tmi.2014.2377694 pmid:25494501 pmcid:PMC4833122 fatcat:csrnfqc4i5eilh7wk5howvpr4u

Whole brain segmentation with full volume neural network

Yeshu Li, Jonathan Cui, Yilun Sheng, Xiao Liang, Jingdong Wang, Eric I-Chao Chang, Yan Xu
2021 Computerized Medical Imaging and Graphics  
Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest.  ...  Existing solutions, usually segment the brain image by classifying the voxels, or labeling the slices or the sub-volumes separately.  ...  Moreover, applying our framework to multi-modal brain segmentation with T2-weighted volumes and super-resolution segmentation with 7T brain MRI volumes are worth investigation.  ... 
doi:10.1016/j.compmedimag.2021.101991 pmid:34634548 fatcat:c4wq6dnmxvfyllgmkra7j3dg3i

An Enhanced Optimal Technique For Accurate Detection Of Color Face Images With Different Illuminations

Ms. Meenakshi Shunmugam , Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
Illuminate invariant features and locality preserving projection approach is exploited for segmented image recognition.  ...  As a final step, Fuzzy neural network is deployed for precise prediction on the basis of locality preserving projection approach results.  ...  MR brain image has classified through Fuzzy Neural Network (FNN), whose outcomes have categorized as Alzheimer's disease, Mild Alzheimer's disease and Huntington's disease.  ... 
doi:10.17762/turcomat.v12i2.1355 fatcat:cjkbbltbizafdkjdtb3zp4zpkm

Computer-Assisted Analysis of Biomedical Images [article]

Leonardo Rundo
2021 arXiv   pre-print
As a matter of fact, the proposed computer-assisted bioimage analysis methods can be beneficial for the definition of imaging biomarkers, as well as for quantitative medicine and biology.  ...  Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications.  ...  [633] presented an Improved Fuzzy Entropy Clustering (IFEC) algorithm to segment brain MR images, characterized by noisy data.  ... 
arXiv:2106.04381v1 fatcat:osqiyd3sbja3zgrby7bf4eljfm

MRI Cross-Modality Image-to-Image Translation

Qianye Yang, Nannan Li, Zixu Zhao, Xingyu Fan, Eric I-Chao Chang, Yan Xu
2020 Scientific Reports  
are important for resolving the challenging complexity in brain structures.  ...  We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images.  ...  61 contains multi-contrast MR images from 23 infants, including T1, T2 images and corresponding labels of Grey Matter (gm) and White Matter (wm).  ... 
doi:10.1038/s41598-020-60520-6 pmid:32111966 pmcid:PMC7048849 fatcat:gimqzl7w2vfnjf4iisypuig2kq

2018 Index IEEE Transactions on Biomedical Engineering Vol. 65

2018 IEEE Transactions on Biomedical Engineering  
., A Delayed-Excitation Data Acquisition Method for High-Frequency Ultrasound Imaging; TBME Jan. 2018 15-20 Qu, X., see Lu, H., TBME April 2018 809-820 Quigley, K.S., see Kleckner, I.R., TBME July 2018  ...  ., Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing; TBME Nov.  ...  ., +, TBME Feb. 2018 378-389 Super-Resolution Axial Localization of Ultrasound Scatter Using Multi-Focal Imaging.  ... 
doi:10.1109/tbme.2018.2890522 fatcat:xoblnegncrgmbmiu3r3hxntrxy

A review on automatic fetal and neonatal brain MRI segmentation

Antonios Makropoulos, Serena J. Counsell, Daniel Rueckert
2018 NeuroImage  
Challenges relating to the image acquisition, the rapid brain development as well as the limited availability of imaging data however hinder this segmentation task.  ...  In recent years, a variety of segmentation methods have been proposed for automatic delineation of the fetal and neonatal brain MRI.  ...  segmentation of Eskildsen et al. (2012) 13 to extract the brain from fetal MR images.  ... 
doi:10.1016/j.neuroimage.2017.06.074 pmid:28666878 fatcat:7fbaimevcfc67eqpdi2zx7l5ey

A hybrid deep learning-improved BAT optimization algorithm for soil classification using remote sensing hyperspectral features

Ujjal Roy
2021 figshare.com  
The typical flow diagram of MR image segmentation is shown in figure1. Experts manually perform the detection of a brain tumor.  ...  The MRI brain and liver image features were extracted successfully. The contrast and correlation of brain MR images were higher than those of liver MR images.  ...  When considering the cost of sensor units distance, angle of inclination and width of sensors will also be included.  ... 
doi:10.6084/m9.figshare.16943230.v1 fatcat:mxxls6u2ufgxrieromvsxwgxni

MRI Cross-Modality NeuroImage-to-NeuroImage Translation [article]

Qianye Yang, Nannan Li, Zixu Zhao, Xingyu Fan, Eric I-Chao Chang, Yan Xu
2018 arXiv   pre-print
Keywords: image-to-image, cross-modality, registration, segmentation, brain MRI  ...  are important for resolving the challenging complexity in brain structures.  ...  ., 2015) contains multi-contrast MR images from 20 subjects, including T1 and T2-Flair images.  ... 
arXiv:1801.06940v2 fatcat:bmsizxutvffh5dgk47mxnmsb24

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey

Nagaraj Yamanakkanavar, Jae Young Choi, Bumshik Lee
2020 Sensors  
Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI).  ...  A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders.  ...  obtain multi-channel characterization for each position within the brain.  ... 
doi:10.3390/s20113243 pmid:32517304 fatcat:bfd5dffy4vbktnsoroi5o2je2a

A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis

Ahmad Naeem, Tayyaba Anees, Rizwan Ali Naqvi, Woong-Kee Loh
2022 Journal of Personalized Medicine  
This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods.  ...  Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients.  ...  [32] proposed a method that uses a CNN with extreme learning and fuzzy c-means with super-resolution, whereby the brain tumor was segmented using fuzzy c-means for the detection of the pre-trained tumor  ... 
doi:10.3390/jpm12020275 pmid:35207763 pmcid:PMC8880689 fatcat:xat6ux65mvbvldid4frrq6v6jy

2019 Index IEEE Transactions on Biomedical Engineering Vol. 66

2019 IEEE Transactions on Biomedical Engineering  
., +, Evaluation of Submillimeter Diffusion Imaging of the Macaque Brain Using Readout-Segmented EPI at 7 T.  ...  ., +, TBME July 2019 1915-1926 A Novel Extension to Fuzzy Connectivity for Body Composition Analy- sis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation.  ... 
doi:10.1109/tbme.2020.2964087 fatcat:mdfzsmdahnao5ccnuj232hycsm
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