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Hairong Qi - IEEE Xplore Author Profile

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Author details

Hairong Qi

Also published under: H. Qi

Publications

155

Citations

6,614

Publications by Year

20002024

Co-Authors:

Mohamed Abdel-MottalebSarina AdhikariAli AdibiAustin AlbrightRaul A. Almeida

Show All Co-Authors (196)

Hairong Qi

Also published under: H. Qi

Affiliation

The University of Tennessee

Knoxville

TN, USA

Publication Topics

Deep Learning,
High-level Features,
Image Classification,
Spatial Information,
Benchmark Datasets,
Convolutional Neural Network,
Object Detection,
Acquisition Conditions,
Attention Map,
Benchmark Networks,
Convolutional Layers,
Cross-entropy Loss

Biography

Hairong Qi (Fellow, IEEE) received the B.S. and M.S. degrees in computer science from Northern Jiaotong University, Beijing, China, in 1992 and 1995, respectively, and the Ph.D. degree in computer engineering from North Carolina State University, Raleigh, NC, USA, in 1999.,She is currently the Gonzalez Family Professor with the Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA. Her current research interests include advanced imaging and collaborative processing in resource-constrained distributed environment, hyperspectral image analysis, and bioinformatics.(Based on document published on 22 October 2021).

Publications

155

Citations

6,614

Publications by Year

20002024

Co-Authors:

Mohamed Abdel-Mottaleb
Sarina Adhikari
Ali Adibi
Austin Albright
Raul A. Almeida

Show All Co-Authors (196)

Author's Published Works

Search History

Showing 1-25 of 6,214 resultsfor "Index Terms":Brain MRI Images

Conferences (5,042)

Journals (1,137)

Magazines (20)

Early Access Articles (15)

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Shanghai Clinical Research and Trial Center, Shanghai, China(19)
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India(18)
School of Biomedical Engineering, ShanghaiTech University, Shanghai, China(17)
Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea(15)
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Results

Noise-resilient edge detection algorithm for brain MRI images

Sos Agaian;Ali Almuntashri

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2009 | Conference Paper |

Cited by: Papers (10)

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In this paper we introduce a noise-resilient edge detection algorithm for brain MRI images. Also, an improved edge detection based on Canny edge detection algorithm is proposed. Computer simulations show that the proposed algorithm is resilient to impulsive noise which makes up for the disadvantages of Canny algorithm, and can detect more edges of MRI brain images effectively. Also, the concept of...Show More

Noise-resilient edge detection algorithm for brain MRI images

Sos Agaian;Ali Almuntashri

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2009 | Conference Paper |

Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review

Toufique A. Soomro;Lihong Zheng;Ahmed J. Afifi;Ahmed Ali;Shafiullah Soomro;Ming Yin;Junbin Gao

IEEE Reviews in Biomedical Engineering

Year: 2023 | Volume: 16 | Journal Article |

Cited by: Papers (119)

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Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a c...Show More

Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review

Toufique A. Soomro;Lihong Zheng;Ahmed J. Afifi;Ahmed Ali;Shafiullah Soomro;Ming Yin;Junbin Gao

IEEE Reviews in Biomedical Engineering

Year: 2023 | Volume: 16 | Journal Article |

Framework for a Low-Cost Intra-Operative Image-Guided Neuronavigator Including Brain Shift Compensation

M. Bucki;C. Lobos;Y. Payan

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2007 | Conference Paper |

Cited by: Papers (5) | Patents (7)

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In this paper we present a methodology to address the problem of brain tissue deformation referred to as 'brain-shift'. This deformation occurs throughout a neurosurgery intervention and strongly alters the accuracy of the neuronavigation systems used to date in clinical routine which rely solely on pre-operative patient imaging to locate the surgical target, such as a tumour or a functional area....Show More

Framework for a Low-Cost Intra-Operative Image-Guided Neuronavigator Including Brain Shift Compensation

M. Bucki;C. Lobos;Y. Payan

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2007 | Conference Paper |

Semantic Segmentation of Brain Tumor from MRI Images and SVM Classification using GLCM Features

Ashfaq Hussain;Ajay Khunteta

2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)

Year: 2020 | Conference Paper |

Cited by: Papers (40)

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The brain tumor is a disease that affects or harms the brain with unwanted tissues. This is very difficult to detect brain tumor tissue from whole brain. Early detection of tumor is very important to save patient's life. Detection or segmentation techniques are used to detect and segment the brain-tumor region from the MRI images of brain and it is very useful method in recent days. In medical, ma...Show More

Semantic Segmentation of Brain Tumor from MRI Images and SVM Classification using GLCM Features

Ashfaq Hussain;Ajay Khunteta

2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)

Year: 2020 | Conference Paper |

Brain Tumour Localization using Image Processing Techniques

Prabira Kumar Sethy;Baby Barnali Pattnaik;Srikanta Dash

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Brain tumor is an abnormal mass or tissue that grows within the brain or its surroundings. Majorly there are three types of brain tumors i.e., Glioma, Meningioma, and Pituitary. As per their origin for the medical practitioner. It is very much required to position the tumor within the brain and its surroundings so that the necessary diagnosis can take place in this research. We considered T1 weigh...Show More

Brain Tumour Localization using Image Processing Techniques

Prabira Kumar Sethy;Baby Barnali Pattnaik;Srikanta Dash

2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

Year: 2022 | Conference Paper |

The Development of Brain Connectivity Browser by Tractography of QBI

Yi-Ping Chao;Chia-Yen Yang;Kuan-Hung Cho;Chun-Hung Yeh;Kun-Hsien Chou;Jyh-Horng Chen;Ching-Po Lin

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2007 | Conference Paper |

Cited by: Papers (1)

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Diffusion MRI is a technique that can probe diffusivity of water molecules in brain structure non-invasively and so that it presents complex neuronal orientation by discrete ribbons. In the study, a new tractography algorithm for high angular resolution diffusion imaging was developed to connect the discrete neural orientations and to extract sophisticated neuronal connectivity in human brain. By ...Show More

The Development of Brain Connectivity Browser by Tractography of QBI

Yi-Ping Chao;Chia-Yen Yang;Kuan-Hung Cho;Chun-Hung Yeh;Kun-Hsien Chou;Jyh-Horng Chen;Ching-Po Lin

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2007 | Conference Paper |

Medical Transformer: Universal Encoder for 3-D Brain MRI Analysis

Eunji Jun;Seungwoo Jeong;Da-Woon Heo;Heung-Il Suk

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 12 | Journal Article |

Cited by: Papers (4)

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Transfer learning has attracted considerable attention in medical image analysis because of the limited number of annotated 3-D medical datasets available for training data-driven deep learning models in the real world. We propose Medical Transformer, a novel transfer learning framework that effectively models 3-D volumetric images as a sequence of 2-D image slices. To improve the high-level repre...Show More

Medical Transformer: Universal Encoder for 3-D Brain MRI Analysis

Eunji Jun;Seungwoo Jeong;Da-Woon Heo;Heung-Il Suk

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 12 | Journal Article |

Review of brain tumor detection from MRI images

Deepa;Akansha Singh

2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)

Year: 2016 | Conference Paper |

Cited by: Papers (3)

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Today image processing plays an important role in medical field and medical imaging is a growing and challenging field. Medical imaging is advantageous in diagnosis of the disease. Many people suffer from brain tumor, it is a serious and dangerous disease. Medical imaging provides proper diagnosis of brain tumor. There are many techniques to detect brain tumor from MRI images. These methods face c...Show More

Review of brain tumor detection from MRI images

Deepa;Akansha Singh

2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)

Year: 2016 | Conference Paper |

A Deep Learning Model for Brain Tumour Classification: An Advanced Analysis of Multi-Modal MRI Integration

Banu S;S Safia Naveed

2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

Year: 2024 | Conference Paper |

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This research discusses recent breakthroughs in brain tumour categorisation using MRI and a deep learning algorithm. Traditional brain tumour classifications cannot characterise complicated tumours similarly or use multimodal MRI data extensively. To overcome these constraints, Structural MRI, Functional MRI, Diffusion-Weighted Imaging (DWI), and Magnetic Resonance Spectroscopy (MRS) are rigorousl...Show More

A Deep Learning Model for Brain Tumour Classification: An Advanced Analysis of Multi-Modal MRI Integration

Banu S;S Safia Naveed

2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

Year: 2024 | Conference Paper |

Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms

Xiao Han;Bruce Fischl

IEEE Transactions on Medical Imaging

Year: 2007 | Volume: 26, Issue: 4 | Journal Article |

Cited by: Papers (166)

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Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based...Show More

Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms

Xiao Han;Bruce Fischl

IEEE Transactions on Medical Imaging

Year: 2007 | Volume: 26, Issue: 4 | Journal Article |

Segmentation Techniques for MRI Brain Tumour Images: A Critical Study

Aichwajya Bora;Nawab Wasim Rahman;Dibya Jyoti Bora

2018 International Conference on Research in Intelligent and Computing in Engineering (RICE)

Year: 2018 | Conference Paper |

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This paper critically examines the different segmentation techniques adopted for segmentation of MRI brain Tumour images. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour. This study has ...Show More

Segmentation Techniques for MRI Brain Tumour Images: A Critical Study

Aichwajya Bora;Nawab Wasim Rahman;Dibya Jyoti Bora

2018 International Conference on Research in Intelligent and Computing in Engineering (RICE)

Year: 2018 | Conference Paper |

Brain Tumor Detection Using Image Processing

Manjunath S;J. Umamaheswari;Sneha Soni;Ahimsa Bhardwaj;Nikita Joshi Mishra;Abhishek Tripathi;Avinav Pathak;Mohit Gupta

2024 International Conference on Computing, Sciences and Communications (ICCSC)

Year: 2024 | Conference Paper |

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Brain tumour detection through medical Imaging is the most challenging and time-consuming procedure. A medical specialist may use magnetic resonance imaging, also known as magnetic resonance imaging, as a diagnostic technique to show the internal structure of the tissue. Brain tumors can be determined using magnetic resonance imaging, which offers detailed information about sensitive human tissue....Show More

Brain Tumor Detection Using Image Processing

Manjunath S;J. Umamaheswari;Sneha Soni;Ahimsa Bhardwaj;Nikita Joshi Mishra;Abhishek Tripathi;Avinav Pathak;Mohit Gupta

2024 International Conference on Computing, Sciences and Communications (ICCSC)

Year: 2024 | Conference Paper |

DeepVolume: Brain Structure and Spatial Connection-Aware Network for Brain MRI Super-Resolution

Zeju Li;Jinhu Yu;Yuanyuan Wang;Hanzhang Zhou;Haowei Yang;Zhongwei Qiao

IEEE Transactions on Cybernetics

Year: 2021 | Volume: 51, Issue: 7 | Journal Article |

Cited by: Papers (17)

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Thin-section magnetic resonance imaging (MRI) can provide higher resolution anatomical structures and more precise clinical information than thick-section images. However, thin-section MRI is not always available due to the imaging cost issue. In multicenter retrospective studies, a large number of data are often in thick-section manner with different section thickness. The lack of thin-section da...Show More

DeepVolume: Brain Structure and Spatial Connection-Aware Network for Brain MRI Super-Resolution

Zeju Li;Jinhu Yu;Yuanyuan Wang;Hanzhang Zhou;Haowei Yang;Zhongwei Qiao

IEEE Transactions on Cybernetics

Year: 2021 | Volume: 51, Issue: 7 | Journal Article |

Validation of Non-Rigid Registration Between Functional and Anatomical Magnetic Resonance Brain Images

Ali Gholipour;Nasser Kehtarnavaz;Richard W. Briggs;Kaundinya S. Gopinath;Wendy Ringe;Anthony Whittemore;Sergey Cheshkov;Khamid Bakhadirov

IEEE Transactions on Biomedical Engineering

Year: 2008 | Volume: 55, Issue: 2 | Journal Article |

Cited by: Papers (22)

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This paper presents a set of validation procedures for nonrigid registration of functional EPI to anatomical MRI brain images. Although various registration techniques have been developed and validated for high-resolution anatomical MRI images, due to a lack of quantitative and qualitative validation procedures, the use of nonrigid registration between functional EPI and anatomical MRI images has ...Show More

Validation of Non-Rigid Registration Between Functional and Anatomical Magnetic Resonance Brain Images

Ali Gholipour;Nasser Kehtarnavaz;Richard W. Briggs;Kaundinya S. Gopinath;Wendy Ringe;Anthony Whittemore;Sergey Cheshkov;Khamid Bakhadirov

IEEE Transactions on Biomedical Engineering

Year: 2008 | Volume: 55, Issue: 2 | Journal Article |

A Review of Machine Learning Techniques for Brain Cancer Diagnosis

Shalini Verma;Kuldeep Singh Kaswan;Sunil Kumar Bharti

2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE)

Year: 2024 | Conference Paper |

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Brain tumors pose a significant threat to life and disrupt the normal functioning of the human body. Early detection is key to precise diagnosis and effective treatment planning. MRI, or Magnetic resonance imaging is instrumental, in identifying, assessing, and tracking brain tumors. However, a definitive diagnosis still depends on surgical pathology. Therefore, this work focusses on applying mach...Show More

A Review of Machine Learning Techniques for Brain Cancer Diagnosis

Shalini Verma;Kuldeep Singh Kaswan;Sunil Kumar Bharti

2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE)

Year: 2024 | Conference Paper |

An automated toolchain for quantitative characterisation of structural connectome from MRI based on non-anatomical cortical parcellation

Sarbani Das;Koushik Maharatna

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2020 | Conference Paper |

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Brain connectivity analysis is a new multidisciplinary approach in neuroscience for determining neurological disorders from brain imaging data. But, there is no end-to-end toolchain that processes raw MRI data and extracts brain connectivity network metrics. Again, the existing method of cortical parcellation from MRI data is mainly based on fixed Brodmann atlas; which does not support neonate's b...Show More

An automated toolchain for quantitative characterisation of structural connectome from MRI based on non-anatomical cortical parcellation

Sarbani Das;Koushik Maharatna

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2020 | Conference Paper |

Stain the Phony Brains: Generating Synthetic Different Medical Images Modalities Using Latent Diffusion Models

Khawla Hussein Ali

2024 International Conference on Electrical, Computer and Energy Technologies (ICECET

Year: 2024 | Conference Paper |

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Medical image synthesis is gaining popularity using generative models. latent diffusion models have recently shown promise in producing remarkably life-like representations of items. However, their ability to produce medical imagery has not yet been investigated. We investigate the possibility of latent diffusion models for the synthesis of different medical modalities. Initially, we create MRI im...Show More

Stain the Phony Brains: Generating Synthetic Different Medical Images Modalities Using Latent Diffusion Models

Khawla Hussein Ali

2024 International Conference on Electrical, Computer and Energy Technologies (ICECET

Year: 2024 | Conference Paper |

An Advanced Brain Tumour Detection Framework Using Improved Fast Mask R-CNN Techniques for Enhanced Accuracy

G. Shankar;V.R. Abhishek Kumar;Akkaru Srikanth;Thummala Jathin Reddy;Shaik Sahil

2024 International Conference on System, Computation, Automation and Networking (ICSCAN)

Year: 2024 | Conference Paper |

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To enhance the precision of diagnosis, this research provides a new structure for identifying brain tumors that integrates an Improved Fast Mask Region based Convolutional Neural Network (IFMRCNN) with complex image processing algorithms. Identification of brain tumors is still a significant difficulty in medical imaging since successful therapy depends on early and accurate identification. By pre...Show More

An Advanced Brain Tumour Detection Framework Using Improved Fast Mask R-CNN Techniques for Enhanced Accuracy

G. Shankar;V.R. Abhishek Kumar;Akkaru Srikanth;Thummala Jathin Reddy;Shaik Sahil

2024 International Conference on System, Computation, Automation and Networking (ICSCAN)

Year: 2024 | Conference Paper |

3-D Brain MRI Tissue Classification on FPGAs

Jahyun J. Koo;Alan C. Evans;Warren J. Gross

IEEE Transactions on Image Processing

Year: 2009 | Volume: 18, Issue: 12 | Journal Article |

Cited by: Papers (25)

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Many automatic algorithms have been proposed for analyzing magnetic resonance imaging (MRI) data sets. With the increasingly large data sets being used in brain mapping, there has been a significant rise in the need for accelerating these algorithms. Partial volume estimation (PVE), a brain tissue classification algorithm for MRI, was implemented on a field-programmable gate array (FPGA)-based hig...Show More

3-D Brain MRI Tissue Classification on FPGAs

Jahyun J. Koo;Alan C. Evans;Warren J. Gross

IEEE Transactions on Image Processing

Year: 2009 | Volume: 18, Issue: 12 | Journal Article |

Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

Amir Alansary;Marwa Ismail;Ahmed Soliman;Fahmi Khalifa;Matthew Nitzken;Ahmed Elnakib;Mahmoud Mostapha;Austin Black;Katie Stinebruner;Manuel F. Casanova;Jacek M. Zurada;Ayman El-Baz

IEEE Journal of Biomedical and Health Informatics

Year: 2016 | Volume: 20, Issue: 3 | Journal Article |

Cited by: Papers (37)

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In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model [a two-level Markov-Gibbs random field (MGRF)] that serves to learn the visual appearance of the brain texture, and a geometric model (the brain isosurfaces) that preserves the brain geometry during the e...Show More

Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

Amir Alansary;Marwa Ismail;Ahmed Soliman;Fahmi Khalifa;Matthew Nitzken;Ahmed Elnakib;Mahmoud Mostapha;Austin Black;Katie Stinebruner;Manuel F. Casanova;Jacek M. Zurada;Ayman El-Baz

IEEE Journal of Biomedical and Health Informatics

Year: 2016 | Volume: 20, Issue: 3 | Journal Article |

Generation of medical atlas from brain MR images through segmentation

Hema P Menon;Majo John;K A Narayanankutty

2010 International Conference on Intelligent and Advanced Systems

Year: 2010 | Conference Paper |

Cited by: Papers (1)

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This work aims at creating a structural atlas for brain MR images, which would help to solve clinical problems, faced during the training periods and can also be referred as a data set for medical diagnosis. Medical images taken as inputs are correlated with predefined atlas image for diagnosing the presence of anomalies. The images are segmented and labeled by using various techniques like thresh...Show More

Generation of medical atlas from brain MR images through segmentation

Hema P Menon;Majo John;K A Narayanankutty

2010 International Conference on Intelligent and Advanced Systems

Year: 2010 | Conference Paper |

Causal Markov random field for brain MR image segmentation

Qolamreza R. Razlighi;Aleksey Orekhov;Andrew Laine;Yaakov Stern

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2012 | Conference Paper |

Cited by: Patents (1)

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We propose a new Bayesian classifier, based on the recently introduced causal Markov random field (MRF) model, Quadrilateral MRF (QMRF). We use a second order inhomogeneous anisotropic QMRF to model the prior and likelihood probabilities in the maximum a posteriori (MAP) classifier, named here as MAP-QMRF. The joint distribution of QMRF is given in terms of the product of two dimensional clique di...Show More

Causal Markov random field for brain MR image segmentation

Qolamreza R. Razlighi;Aleksey Orekhov;Andrew Laine;Yaakov Stern

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2012 | Conference Paper |

Histogram Equalization for Image Enhancement Using MRI Brain Images

N. Senthilkumaran;J. Thimmiaraja

2014 World Congress on Computing and Communication Technologies

Year: 2014 | Conference Paper |

Cited by: Papers (83)

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Medical image processing plays an essential role in providing information in wide area for such advanced images. Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. MRI of the brain is an invaluable tool to help physicians to diagnose and treat various brain diseases including stroke, cancer, and epilepsy. The sp...Show More

Histogram Equalization for Image Enhancement Using MRI Brain Images

N. Senthilkumaran;J. Thimmiaraja

2014 World Congress on Computing and Communication Technologies

Year: 2014 | Conference Paper |

Computer-assisted segmentation of brain tumor lesions from multi-sequence Magnetic Resonance Imaging using the Mumford-Shah model

Jihan M. Zoghbi;Marcelo H. Mamede;Marcel P. Jackowski

2010 25th International Conference of Image and Vision Computing New Zealand

Year: 2010 | Conference Paper |

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Segmentation of brain lesions in Magnetic Resonance Imaging (MRI) is a difficult task to be mastered by the specialist. This is due to the presence of noise, partial volume effects and susceptibility artifacts in the images and on the borders of the regions of interest. These problems can interfere with the results when manual segmentation is used. Manual segmentation uses local anatomic informati...Show More

Computer-assisted segmentation of brain tumor lesions from multi-sequence Magnetic Resonance Imaging using the Mumford-Shah model

Jihan M. Zoghbi;Marcelo H. Mamede;Marcel P. Jackowski

2010 25th International Conference of Image and Vision Computing New Zealand

Year: 2010 | Conference Paper |

Integration of Contourlet Transform and Canny Edge Detector for Brain Image Segmentation

Lata Ayesha Akter;Goo-Rak Kwon

2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)

Year: 2018 | Conference Paper |

Cited by: Papers (1)

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This paper proposed a new integrated image segmentation method for MRI brain images. In this method we have used a new transformation called Contourlet Transform which is integrated with canny edge detector. For a better segmentation we have applied an enhancement function on the contourlet coefficients before applying canny edge detector. The experimental results shows that using canny edge detec...Show More

Integration of Contourlet Transform and Canny Edge Detector for Brain Image Segmentation

Lata Ayesha Akter;Goo-Rak Kwon

2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)

Year: 2018 | Conference Paper |

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