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- research-articleJanuary 2022
A fuzzy membership based comparison of the grey matter (GM) in cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using brain images
- Ruhul Amin Hazarika,
- Arnab Kumar Maji,
- Samarendra Nath Sur,
- Iustin Olariu,
- Debdatta Kandar,
- Valentina Emilia Balas
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology (JIFS), Volume 43, Issue 22022, Pages 1779–1792https://doi.org/10.3233/JIFS-219279Grey matter (GM) in human brain contains most of the important cells covering the regions involved in neurophysiological operations such as memory, emotions, decision making, etc. Alzheimer’s disease (AD) is a neurological disease that kills the brain ...
- articleJanuary 2017
Bayesian tensor regression
We propose a Bayesian approach to regression with a scalar response on vector and tensor covariates. Vectorization of the tensor prior to analysis fails to exploit the structure, often leading to poor estimation and predictive performance. We introduce ...
- research-articleJanuary 2016
Contourlet transform based subband normalization for region based medical image compression
Intelligent Decision Technologies (INTDTEC), Volume 10, Issue 42016, Pages 385–391https://doi.org/10.3233/IDT-160265Low bit rate compression approach has been proposed for easy transmission of medical image from one place to another. Contourlet transform technique is used for obtaining multi directional subbands and for capturing the fine details of the image. Transformed ...
- research-articleNovember 2015
Robust Estimation of Unbalanced Mixture Models on Samples with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence (ITPM), Volume 37, Issue 11Nov. 2015, Pages 2273–2285https://doi.org/10.1109/TPAMI.2015.2404835Mixture models are often used to compactly represent samples from heterogeneous sources. However, in real world, the samples generally contain an unknown fraction of outliers and the sources generate different or unbalanced numbers of observations. Such ...
- ArticleSeptember 2013
Atlas Based Intensity Transformation of Brain MR Images
Proceedings of Third International Workshop on Multimodal Brain Image Analysis - Volume 8159September 2013, Pages 51–62https://doi.org/10.1007/978-3-319-02126-3_6Magnetic resonance imaging (MRI) is a noninvasive modality that has been widely used to image the structure of the human brain. Unlike reconstructed x-ray computed tomography images, MRI intensities do not possess a calibrated scale, and the images ...
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- research-articleDecember 2012
A new denoising filter for brain MR images
ICVGIP '12: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image ProcessingDecember 2012, Article No.: 57, Pages 1–8https://doi.org/10.1145/2425333.2425390A new denoising filter is proposed for human brain MR image. The proposed filter is based on the notion of existing bilateral filter whose objective is to get a noise-free smooth image, preserving edges and other features intact. We have introduced a ...
- research-articleOctober 2011
Magnetic field distribution of birdcage coil for 4 T MRI system with no lumped circuit elements
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication TechnologiesOctober 2011, Article No.: 12, Pages 1–3https://doi.org/10.1145/2093698.2093710Recently, Magnetic Resonance Imaging (MRI) system which operates up to under 3 T is being used in clinical practice in Japan. In order to achieve the requirement of obtaining more high-quality images and short imaging time, devices utilize high magnetic ...
- ArticleOctober 2011
Clonal selection algorithm for gaussian mixture model based segmentation of 3d brain MR images
IScIDE'11: Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data EngineeringOctober 2011, Pages 295–302https://doi.org/10.1007/978-3-642-31919-8_38Evolutionary algorithms with global search capabilities have been successfully used to replace local search heuristics in statistical image segmentation. Among them, a novel immune-inspired evolutionary method, clonal selection algorithm (CSA) has ...
- ArticleSeptember 2011
Myocardial contractility and regional work throughout the cardiac cycle using FEM and MRI
STACOM'11: Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challengesSeptember 2011, Pages 149–159https://doi.org/10.1007/978-3-642-28326-0_15The role of myocardial contractile force in the progression of cardiovascular diseases such as heart failure (HF) has been the focus of many studies. In order to better understand the mechanisms underlying compromised contractility, finite element (FE) ...
- ArticleAugust 2011
Spatio-temporal Resolution Enhancement of Vocal Tract MRI Sequences Based on the Wiener Filter
SIBGRAPI '11: Proceedings of the 2011 24th SIBGRAPI Conference on Graphics, Patterns and ImagesAugust 2011, Pages 242–249https://doi.org/10.1109/SIBGRAPI.2011.33Research on human speech production is highly dependent upon information about the position and movements of the speech articulators. Dynamic magnetic resonance imaging (MRI) has been the main tool to support this process. With this technique, image ...
- ArticleJuly 2011
Evaluation method for MRI brain tissue abnormalities segmentation study
Proceedings of the 15th WSEAS international conference on ComputersJuly 2011, Pages 297–302Segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research as it can facilitates the neurological diseases diagnosis. However, there ...
- ArticleJuly 2011
Brain abnormalities segmentation performances contrasting: adaptive network-based fuzzy inference system (ANFIS) vs K-nearest neighbors (k-NN) vs fuzzy c-means (FCM)
Proceedings of the 15th WSEAS international conference on ComputersJuly 2011, Pages 285–290Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper contrasts the performances of Adaptive Network-Based Fuzzy Inference System (ANFIS), k-Nearest Neighbors (k-NN) and Fuzzy c-Means (...
- ArticleJuly 2011
A compressed sensing approach for MR tissue contrast synthesis
IPMI'11: Proceedings of the 22nd international conference on Information processing in medical imagingJuly 2011, Pages 371–383The tissue contrast of a magnetic resonance (MR) neuroimaging data set has a major impact on image analysis tasks like registration and segmentation. It has been one of the core challenges of medical imaging to guarantee the consistency of these tasks ...
- research-articleMarch 2011
Automated Detection of White Matter Changes in Elderly People Using Fuzzy, Geostatistical, and Information Combining Models
IEEE Transactions on Information Technology in Biomedicine (TITB), Volume 15, Issue 2March 2011, Pages 242–250https://doi.org/10.1109/TITB.2010.2081996Detection of white matter changes of the brain using magnetic resonance imaging (MRI) has increasingly been an active and challenging research area in computational neuroscience. There have rarely been any single image analysis methods that can ...
- research-articleJanuary 2011
Multiscale Amplitude-Modulation Frequency-Modulation (AM–FM) Texture Analysis of Multiple Sclerosis in Brain MRI Images
IEEE Transactions on Information Technology in Biomedicine (TITB), Volume 15, Issue 1January 2011, Pages 119–129https://doi.org/10.1109/TITB.2010.2091279This study introduces the use of multiscale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis (MS) using magnetic resonance (MR) images from brain. Clinically, there is interest in identifying potential ...
- ArticleNovember 2010
Comparison of multispectral image processing techniques to brain MR image classification between fuzzy C-mean method and geodesic active contours of Caselles level set method
ICCCI'10: Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartINovember 2010, Pages 491–498Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water, proteinaceous fluid, soft tissue and other tissues with a variety of contrast. The abundance ...
- ArticleSeptember 2010
Cardiac active contraction parameters estimated from magnetic resonance imaging
STACOM'10/CESC'10: Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challengeSeptember 2010, Pages 194–203Impaired systolic ventricular function is common in patients diagnosed with heart failure (HF) or ischaemic heart disease. The diminished contractile performance with impaired contractility (systolic HF) can be induced by impaired filling function (...
- ArticleSeptember 2010
Bayesian classification using DCT features for brain tumor detection
KES'10: Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part ISeptember 2010, Pages 340–349Mortality rate by the brain tumor was very high some years before. But now this rate is decreased in the recent years due to the earlier diagnosis and proper treatment. Chances of the long survival of the patient can be increased by the accurate brain ...
- research-articleMay 2010
Wiimote-controlled stereoscopic MRI visualization with sonic augmentation
Futureplay '10: Proceedings of the International Academic Conference on the Future of Game Design and TechnologyMay 2010, Pages 261–262https://doi.org/10.1145/1920778.1920825Medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) that produce three-dimensional views of the body have become commonplace. In this paper we provide a preliminary overview of an MRI stereoscopic ...
- research-articleMay 2010
Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions
IEEE Transactions on Neural Networks (TNN), Volume 21, Issue 5May 2010, Pages 701–717https://doi.org/10.1109/TNN.2010.2041069Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique ...