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.

Research on Fault Diagnosis for Petrochemical Running Equipments Based on ICA

2011 ◽  
Vol 48-49 ◽  
pp. 950-953
Author(s):  
Zhi Gang Chen ◽  
Xiao Jiao Lian ◽  
Ming Zhou
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
New Method ◽  
Signal Separation ◽  
Signal Extraction ◽  
Vibration Signals ◽  
Signal Preprocessing

For solving the difficulty of feature signal extraction from vibration signals, a new method based on Independent Component Analysis (ICA) is proposed to realize separation and filtering for multi-source vibration signals. Firstly, the principal and algorithm of ICA used to separate mixed signals is introduced. Secondly, application in signal separation and filtering with ICA is studied in diagnosis. In addition, imitation and field examples are given. The experiments show it is feasible to separate and extract feature signal from multi-source vibration signals and it is an effective method in signal preprocessing in fault diagnosis.

BIOLOGICAL SIGNAL ANALYSIS BY INDEPENDENT COMPONENT ANALYSIS USING COMPLEX WAVELET TRANSFORM

2010 ◽  
Vol 08 (04) ◽  
pp. 595-608 ◽  
Author(s):  
SATOSHI HORIHATA ◽  
ZHONG ZHANG ◽  
TAKASHI IMAMURA ◽  
TETSUO MIYAKE ◽  
HIROSHI TODA ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Signal Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
New Method ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Emg Signal ◽  
Electrocardiogram Ecg

Independent component analysis (ICA) is a useful method for blind source separation of two or more signals. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT), in which voice and noise signals were separated using a new method. At that time, we used a simulated signal. In this study, we analyze measured biological signals by using a new method, and discuss its effectiveness. As an experiment, we try to separate an electromyogram (EMG) signal from an electrocardiogram (ECG) signal.

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.

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