Spectral clustering independent component analysis for tissue classification from brain MRI

2013 ◽  
Vol 8 (6) ◽  
pp. 667-674 ◽  
Author(s):  
Sindhumol S. ◽  
Anil Kumar ◽  
Kannan Balakrishnan
Keyword(s):  
Independent Component Analysis ◽  
Spectral Clustering ◽  
Brain Mri ◽  
Component Analysis ◽  
Independent Component ◽  
Tissue Classification

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.

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.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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