Gray Matter Segmentation of Brain MRI Using Hybrid Enhanced Independent Component Analysis in Noisy and Noise Free Environment

2020 ◽  
Vol 47 ◽  
pp. 75-103
Author(s):  
Shaik Basheera ◽  
M. Satya Sai Ram
Keyword(s):  
Independent Component Analysis ◽  
Gray Matter ◽  
Brain Mri ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environments ◽  
Low Contrast ◽  
Free Environment ◽  
Medical Segmentation ◽  
The Brain

Medical segmentation is the primary task performed to diagnosis the abnormalities in the human body. The brain is the complex organ and anatomical segmentation of brain tissues is a challenging task. In this paper, we used Enhanced Independent component analysis to perform the segmentation of gray matter. We used modified K means, Expected Maximization and Hidden Markov random field to provide better spatial correlation that overcomes in-homogeneity, noise and low contrast. Our objective is achieved in two steps (i) initially unwanted tissues are clipped from the MRI image using skull stripped Algorithm (ii) Enhanced Independent Component analysis is used to perform the segmentation of gray matter. We apply the proposed method on both T1w and T2w MRI to perform segmentation of gray matter at different noisy environments. We evaluate the the performance of our proposed system with Jaccard Index, Dice Coefficient and Accuracy. We further compared the proposed system performance with the existing frameworks. Our proposed method gives better segmentation of gray matter useful for diagnosis neurodegenerative disorders.

Gray Matter Segmentation of Brain MRI Using Hybrid Enhanced Independent Component Analysis

2021 ◽  
pp. 2150029
Author(s):  
Shaik Basheera ◽  
M. Satya Sai Ram
Keyword(s):  
Independent Component Analysis ◽  
Gray Matter ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Spatial Relation ◽  
Component Analysis ◽  
Independent Component ◽  
Morphological Operations ◽  
Low Contrast ◽  
Skull Stripping

One of the primary pre-processing tasks of medical image analysis is segmentation; it is used to diagnose the abnormalities in the tissues. As the brain is a complex organ, anatomical segmentation of brain tissues is a challenging task. Segmented gray matter is analyzed for early diagnosis of neurodegenerative disorders. In this endeavor, we used enhanced independent component analysis to perform segmentation of gray matter in noise-free and noisy environments. We used modified [Formula: see text]-means, expectation–maximization and hidden Markov random field to provide better spatial relation to overcome inhomogeneity, noise and low contrast. Our objective is achieved using the following two steps: (i) Irrelevant tissues are stripped from the MRI using skull stripping algorithm. In this algorithm, sequence of threshold, morphological operations and active contour are applied to strip the unwanted tissues. (ii) Enhanced independent component analysis is used to perform segmentation of gray matter. The proposed approach is applied on both T1w MRI and T2w MRI images at different noise environments such as salt and pepper noise, speckle noise and Rician noise. We evaluated the performance of the approach using Jaccard index, Dice coefficient and accuracy. The parameters are further compared with existing frameworks. This approach gives better segmentation of gray matter for the diagnosis of atrophy changes in brain MRI.

scholarly journals Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Sindhumol S. ◽  
Anil Kumar ◽  
Kannan Balakrishnan
Keyword(s):  
Independent Component Analysis ◽  
Brain Tissue ◽  
Brain Mri ◽  
Component Analysis ◽  
Extraction Methods ◽  
Clinical Analysis ◽  
Independent Component ◽  
New Method ◽  
Tissue Classification ◽  
Multispectral Data

Multispectral analysis is a potential approach in simultaneous analysis of brain MRI sequences. However, conventional classification methods often fail to yield consistent accuracy in tissue classification and abnormality extraction. Feature extraction methods like Independent Component Analysis (ICA) have been effectively used in recent studies to improve the results. However, these methods were inefficient in identifying less frequently occurred features like small lesions. A new method, Multisignal Wavelet Independent Component Analysis (MW-ICA), is proposed in this work to resolve this issue. First, we applied a multisignal wavelet analysis on input multispectral data. Then, reconstructed signals from detail coefficients were used in conjunction with original input signals to do ICA. Finally, Fuzzy C-Means (FCM) clustering was performed on generated results for visual and quantitative analysis. Reproducibility and accuracy of the classification results from proposed method were evaluated by synthetic and clinical abnormal data. To ensure the positive effect of the new method in classification, we carried out a detailed comparative analysis of reproduced tissues with those from conventional ICA. Reproduced small abnormalities were observed to give good accuracy/Tanimoto Index values, 98.69%/0.89, in clinical analysis. Experimental results recommend MW-ICA as a promising method for improved brain tissue classification.

scholarly journals Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference

NeuroImage ◽  
2013 ◽  
Vol 83 ◽  
pp. 384-396 ◽  
Author(s):  
Jiayu Chen ◽  
Vince D. Calhoun ◽  
Godfrey D. Pearlson ◽  
Nora Perrone-Bizzozero ◽  
Jing Sui ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Gray Matter ◽  
Component Analysis ◽  
Independent Component ◽  
Guided Exploration ◽  
Genomic Risk

An independent component analysis based tool for exploring functional connections in the brain

2009 ◽  
Author(s):  
S. M. Rolfe ◽  
L. Finney ◽  
R. F. Tungaraza ◽  
J. Guan ◽  
L. G. Shapiro ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Functional Connections ◽  
The Brain

scholarly journals Sparse parallel independent component analysis and its application to identify linked genomic and gray matter alterations underlying working memory impairment in attention-deficit/hyperactivity disorder

2020 ◽  
Author(s):  
Kuaikuai Duan ◽  
Jiayu Chen ◽  
Vince D. Calhoun ◽  
Wenhao Jiang ◽  
Kelly Rootes-Murdy ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Working Memory ◽  
Independent Component Analysis ◽  
Psychiatric Disorders ◽  
Gray Matter ◽  
Attention Deficit ◽  
Component Analysis ◽  
Independent Component ◽  
Hyperactivity Disorder ◽  
Common Genetic Variants

AbstractMost psychiatric disorders are highly heritable and associated with altered brain structural and functional patterns. Data fusion analyses on brain imaging and genetics, one of which is parallel independent component analysis (pICA), enable the link of genomic factors to brain patterns. Due to the small to modest effect sizes of common genetic variants in psychiatric disorders, it is usually challenging to reliably separate disorder-related genetic factors from the rest of the genome with the typical size of clinical samples. To alleviate this problem, we propose sparse parallel independent component analysis (spICA) to leverage the sparsity of individual genomic sources. The sparsity is enforced by performing Hoyer projection on the estimated independent sources. Simulation results demonstrate that the proposed spICA yields improved detection of independent sources and imaging-genomic associations compared to pICA. We applied spICA to gray matter volume (GMV) and single nucleotide polymorphism (SNP) data of 341 unrelated adults, including 127 controls, 167 attention-deficit/hyperactivity disorder (ADHD) cases, and 47 unaffected siblings. We identified one SNP source significantly and positively associated with a GMV source in superior/middle frontal regions. This association was replicated with a smaller effect size in 317 adolescents from ADHD families, including 188 individuals with ADHD and 129 unaffected siblings. The association was found to be more significant in ADHD families than controls, and stronger in adults and older adolescents than younger ones. The identified GMV source in superior/middle frontal regions was not correlated with head motion parameters and its loadings (expression levels) were reduced in adolescent (but not adult) individuals with ADHD. This GMV source was associated with working memory deficits in both adult and adolescent individuals with ADHD. The identified SNP component highlights SNPs in genes encoding long non-coding RNAs and SNPs in genes MEF2C, CADM2, and CADPS2, which have known functions relevant for modulating neuronal substrates underlying high-level cognition in ADHD.

scholarly journals Session to Session Transfer Learning Method Using Independent Component Analysis with Regularized Common Spatial Patterns for EEG-MI Signals

2019 ◽  
Vol 15 (1) ◽  
pp. 13-27
Author(s):  
Zaineb Alhakeem ◽  
Ramzy Ali
Keyword(s):  
Independent Component Analysis ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Component Analysis ◽  
Independent Component ◽  
Learning Method ◽  
Common Spatial Pattern ◽  
Brain Signals ◽  
The Brain ◽  
Better Than

Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.

An Independent Component Analysis Based Defect Detection for the OLED Display

2012 ◽  
Vol 605-607 ◽  
pp. 724-728 ◽  
Author(s):  
Zhi Liang Wang ◽  
Jian Gao ◽  
Chuan Xia Jian ◽  
Yao Cong Liang ◽  
Yu Cen
Keyword(s):  
Independent Component Analysis ◽  
Defect Detection ◽  
Detection System ◽  
Component Analysis ◽  
Rapid Identification ◽  
Independent Component ◽  
Display Device ◽  
Low Contrast ◽  
Light Emitting ◽  
Mixing Matrix

Organic Light Emitting Displays (OLED) is a new type of display device which has become increasingly attractive and popular. Due to the complex manufacturing process, various defects may exist on the OLED panel. These defects have the characteristics of fuzzy boundaries, irregular in shape, low contrast with background and they are mixed with the texture background increasing the difficulty of a rapid identification. In this paper, we proposed an approach to detect these defects based on the model of independent component analysis (ICA). The ICA model is applied to a perfect OLED image to determine the de-mixing matrix and its corresponding independent components (ICs). Through the choice of a proper ICi row vector, the new de-mixing matrix is generated which contains only uniform information and is used to reconstruct the OLED background image. The defect result can be obtained by the subtracting operation between the reconstructed background and the source images. The detection system is implemented in the Labview and the testing results show that the ICA based OLED defect detection method is feasible and effective.

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