Now showing 1 - 9 of 9
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    Differentiation of Alzheimer conditions in brain MR images using bidimensional multiscale entropy-based texture analysis of lateral ventricles
    (01-09-2022)
    Veluppal, Amrutha
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    sadhukhan, Deboleena
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    gopinath, Venugopal
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    Alzheimer's Disease (AD) is a progressive fatal neurodegenerative disorder that causes cognitive decline in affected people. Image processing of brain MR images can aid in identifying significant imaging biomarkers for detection of AD and its prodromal stage Mild Cognitive Impairment (MCI). Bidimensional multiscale entropy-based texture analysis is a new approach to quantify the textural variations in images at multiple scales. This work is based on the application of bidimensional multiscale entropy for analyzing AD induced textural alterations in lateral ventricles of the brain MR images. For this T1 weighted MR brain images of normal, MCI and AD subjects are obtained from public database. Lateral ventricles (LV) are delineated using reaction–diffusion level set technique from transaxial image slice with high accuracy. Bidimensional multiscale entropy is then applied on segmented LV to extract entropy features at multiple image scales and complexity indices are evaluated for each scale to study textural variations. The parameters such as tolerance factor, window lengths and scales for computation of multiscale entropy for significant differentiation amongst the healthy and diseased subjects are experimentally evaluated. The obtained entropy values from healthy subjects are observed to be significantly lower from the pathological subjects across scales. Classification with extracted features using a linear discriminant classifier achieves an accuracy of 80.1% and 87.6% for Normal vs MCI and Normal vs AD classes, respectively. The proposed multiscale entropy-based approach captures the textural alterations in lateral ventricles of brain MR images and furthermore, can be used as automated tool for early diagnosis of AD.
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    Laplace Beltrami eigen value based classification of normal and Alzheimer MR images using parametric and non-parametric classifiers
    (15-10-2016)
    Ramaniharan, Anandh Kilpattu
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    Manoharan, Sujatha Chinnaswamy
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    Automated study of brain sub-anatomic region like Corpus Callosum (CC) is challenging due to its complex topology and varying shape. The development of reliable Computer Aided Diagnosis (CAD) systems would help in the early detection of Alzheimer's Disease (AD) and to perform drug trails to palliate the effect of AD. In this work, an attempt has been made to analyse the shape changes of CC using shape based Laplace Beltrami (LB) eigen value features and machine learning techniques. CC from the normal and AD T1-weighted magnetic resonance images are segmented using Reaction Diffusion (RD) level set method and the obtained results are validated against the Ground Truth (GT) images. Ten LB eigen values are extracted from the segmented CC images. LB eigen values are positive sequence of infinite series that describe the intrinsic geometry of objects. These values capture the shape information of CC by solving the eigen value problem of LB operator on the triangular meshes. The significant features are selected based on Information Gain (IG) ranking and subjected to classification using K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Naïve Bayes (NB). The performance of LB eigen values in the AD diagnosis is evaluated using classifiers' accuracy, specificity and sensitivity measures. Results show that, RD level set is able to segment CC in normal and AD images with high percentage of similarity with GT. The extracted LB eigen values are found to show high difference in the mean values between normal and AD subjects with high statistical significance. The LB eigen modes λ2, λ7 and λ8 are identified as prominent features by IG based ranking. KNN is able to give maximum classification accuracy of 93.37% compared to linear SVM and NB classifiers. This value is observed to be high than the results obtained using geometric features. The proposed CAD system focuses solely on the geometric variations of CC extracted using LB eigen value spectrum. The extraction of eigen modes in the LB spectrum is easy to compute, does not involve too many parameters and less time consuming. Thus this CAD study seems to be clinically significant in the shape investigation of brain structures for AD diagnosis.
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    Study of Alzheimer's disease progression in mr brain images based on segmentation and analysis of ventricles using modified drlse method and minkowski functionals
    (01-01-2015)
    Kayalvizh, L. M.
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    Kavitha, G.
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    Sujatha, C. M.
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    In this work, the ventricles in MR brain images are segmented using edge based modified Distance Regularized Level Set Evolution (DRLSE) method and the structural changes in the disease is further analysed using Minkowski functionals (MFs). Twenty normal and abnormal Tl-weighted coronal mid slice MR image are considered for the analysis. The MR brain image is pre-processed using contrast enhancement method. The edge based modified DRLSE with a new penalty term is used to segment the ventricles from the enhanced images. The results of the level set method are compared with geodesic active contour method. The segmentation results are validated using ZSI (Zijdenbos Similarity Index) and F-score. The Minkowski functionals such as MF-area, MF-perimeter and MF-Euler number are calculated from the extracted ventricle region. The longitudinal analysis of ventricles is performed using these features. The results show that the DRLSE based level set method is able to extract the ventricle edges with less discontinuity. The F-score and ZSI is high for DRLSE (0.83 and 0.84) compared to geodesic method (0.79 and 0.80). The MF-area is able to discriminate the controls and the AD subjects with high statistical significance (p < 0.001). This analysis also shows that the MF-area increases with severity. These results could be used for the study of discrimination and progression of the Alzheimer's disease like disorders.
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    Diffusion tensor based Alzheimer image analysis using region specific volume features and random forest classifier
    (01-01-2014)
    Piyush, Ranjan
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    In this work a method to utilize volume information as an independent feature for diagnosis of Alzheimer's disease (AD) using Diffusion Tensor Images (DTI) is proposed. For this study the DTI were obtained from ADNI, an international repository for current Alzheimer's study. Equal number of AD and normal control subjects were used for the study. The volume of six regions namely, Fornix/Stria Terminalis Left, Fornix/Stria Terminalis Right, Fornix, Corpus Callosum, Cerebral Peduncle and Anterior Corona Radiata, reported to be prominently responsible for AD were extracted. Volume features corresponding to the above said feature set was used to classify AD and controls using random forest classifier. The data was also used for classification with significant components extracted using principle component analysis. The classification accuracy of 71.4% was achieved for full dataset which further improved to 85.7% on application of principle components as the feature. An enhancement in recall was observed which increased from 71.4% for full dataset to 88.9% for principle components. The precision was observed to be 88.9%. The results demonstrate that volume can be used as a feature for differentiating AD from normal controls. The volume feature can be used independently for initial screening tests. This can be used for automated identification of AD using DTI.
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    Differentiation of early mild cognitive impairment in brainstem MR images using multifractal detrended moving average singularity spectral features
    (01-03-2020)
    Rohini, P.
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    Brainstem texture analysis can provide valuable information in the diagnosis of Early Mild Cognitive Impairment (EMCI) condition. In this work, 3D brainstem structure is segmented and analysed for texture alterations using multifractal features to differentiate EMCI from other Alzheimer's disease stages. The images obtained from public domain database are preprocessed for spatial registration, skull stripping and contrast enhancement. White matter volume is segmented from the preprocessed images using fuzzy ‘C’ means clustering algorithm. Midsagittal white matter tissue is used as the initial seed to segment the brainstem volume using sparse field level set method. Multifractal detrended moving average algorithm is used to compute the fluctuation function, generalized Hurst exponent and mass exponent to study the multifractal characteristics of brainstem structure. Features extracted from the multifractal spectrum are analysed to differentiate the images pertaining to EMCI subject group. Results indicate that the proposed technique is able to segment the brainstem structure from all the considered images. The fluctuation function is observed to have linear relationship with scale. The generalized Hurst exponent decreases with order and mass exponent follows a non-linear trend demonstrating the multifractal nature of brainstem. Singularity spectral features namely strength of multifractality, Holder exponent at f(2.8), tangent slope and maximum Holder exponent are found to be most significant in differentiating EMCI from subject groups. As this complex EMCI distinction is clinically important, the proposed approach is useful for early diagnosis of Alzheimer's condition.
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    Segmentation and analysis of corpus callosum in Alzheimer mr images using total variation based diffusion filter and level set method
    (01-01-2015)
    Anandh, K. R.
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    Sujatha, C. M.
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    Alzheimer's Disease (AD) is a common form of dementia that affects gray and white matter structures of brain. Manifestation of AD leads to cognitive deficits such as memory impairment problems, ability to think and difficulties in performing day to day activities. Although the etiology of this disease is unclear, imaging biomarkers are highly useful in the early diagnosis of AD. Magnetic resonance imaging is an indispensible non-invasive imaging modality that reflects both the geometry and pathology of the brain. Corpus Callosum (CC) is the largest white matter structure as well as the main inter-hemispheric fiber connection that undergoes regional alterations due to AD. Therefore, segmentation and feature extraction are predominantly essential to characterize the CC atrophy. In this work, an attempt has been made to segment CC using edge based level set method. Prior to segmentation, the images are pre-processed using Total Variation (TV) based diffusion filtering to enhance the edge information. Shape based geometric features are extracted from the segmented CC images to analyze the CC atrophy. Results show that the edge based level set method is able to segment CC in both the normal and AD images. TV based diffusion filtering has performed uniform region specific smoothing thereby preserving the texture and small scale details of the image. Consequently, the edge map of CC in both the normal and AD are apparently sharp and distinct with continuous boundaries. This facilitates the final contour to correctly segment CC from the nearby structures. The extracted geometric features such as area, perimeter and minor axis are found to have the percentage difference of 5.97%, 22.22% and 9.52% respectively in the demarcation of AD subjects. As callosal atrophy is significant in the diagnosis of AD, this study seems to be clinically useful.
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    Characterization of Alzheimer conditions in MR images using volumetric and sagittal brainstem texture features
    (01-05-2019)
    Rohini, P.
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    Background and Objective: Brainstem analysis in Magnetic Resonance Images is essential to detect Alzheimer's condition in the preclinical stages. In this work, an attempt has been made to segment the brainstem in sagittal (2D) and volumetric (3D) images and evaluate texture changes to differentiate Alzheimer's disease (AD) stages. Method: The images obtained from a public access database are spatial normalized, skull stripped and contrast enhanced. Morphological Reconstruction based Fast and Robust Fuzzy ‘C’ Means technique is used to cluster the brain tissue in preprocessed images into three groups namely cerebrospinal fluid, grey matter and white matter. Brainstem is segmented from the white matter tissue using connected component labelling. Texture features from volumetric and sagittal brainstem slices are extracted and its statistical significance is evaluated. Results: Results show that the proposed approach is able to segment the brainstem from all the considered images. Variation in texture is observed to be less than 2% among sagittal brainstem slices. Additionally, midsagittal and volumetric features are correlated, suggesting that midsagittal brainstem structure gives an estimate of brainstem volume. Texture features extracted from midsagittal slice shows significant variation (p < 0.05) and is able to differentiate AD classes. Conclusion: Midsagittal brainstem texture features are able to capture the changes occurring in the early stages of disease condition. As the distinction of AD in preclinical stage is complex and clinically significant, this approach could be useful for early diagnosis of the disease.
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    Publication
    Segmentation of ventricles in Alzheimer MR images using anisotropic diffusion filtering and level set method
    (01-01-2014)
    Anandh, K. R.
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    Sujatha, C. M.
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    Ventricle enlargement is a useful structural biomarker for the diagnosis of Alzheimer's Disease (AD). This devastating neurodegenerative disorder results in progression of dementia. Although AD results in the passive increment of ventricle volume, there exists a large overlap in the volume measurements of AD and normal subjects. Hence, shape based analysis of ventricle dilation is appropriate to detect the subtle morphological changes among these two groups. In this work, segmentation of ventricle in Alzheimer MR images is employed using level set method and anisotropic based diffusion filtering. Images considered for this study are preprocessed using filters. Anisotropic based diffusion filtering is employed to extract the edge map. This filtering performs region specific smoothing process using the diffusion coefficient as a function of image gradient. Filtered images are subjected to level set method which employs an improved diffusion rate equation for the level set evolution. Geometric features are extracted from the segmented ventricles. Results show that the diffusion filter could extract edge map with sharp region boundaries. The modified level set method is able to extract the morphological changes in ventricles. The observed morphological changes are distinct for normal and AD subjects (p < 0.0001). It is also observed that the sizes of ventricle in the AD subjects are noticeably enlarged when compared to normal subjects. Features obtained from the segmented ventricles are also clearly distinct and demonstrate the differences in the AD subjects. As ventricle volume and its morphometry are significant biomarkers, this study seems to be clinically relevant. Copyright 2014, ISA All Rights Reserved.
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    Detection of Mild Cognitive Impairment Using Kernel Density Estimation Based Texture Analysis of the Corpus Callosum in Brain MR Images
    (01-10-2022)
    Veluppal, A.
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    Sadhukhan, D.
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    Gopinath, V.
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    Objectives: Mild Cognitive Impairment (MCI) is the prodromal stage of Alzheimer's disease (AD), which is a progressive and fatal neurodegenerative disorder. Detection of MCI condition can enable early diagnosis resulting in timely intervention to delay the disease progression. Onset of MCI causes tissue alterations in Corpus Callosum (CC) of the brain. Texture analysis of brain Magnetic Resonance (MR) images aids in characterising these imperceptible changes. In this study, Kernel Density Estimation (KDE) technique is used to analyse the textural variations in CC to detect MCI condition. Materials and method: The pre-processed brain MR images are obtained from a public access database. Reaction Diffusion level set is employed to segment CC from sagittal slices of the images. Kernel density estimation method is applied to study the local intensity variations within the segmented CC. Statistical features quantifying these variations are extracted from the KDE values. These features are used to differentiate MCI condition using linear classifiers based on discriminant analysis and support vector machine. The results are compared with conventional Grey Level Co-occurrence Matrix (GLCM) features for validation. Results: The KDE-based texture features extracted from CC show significant variation between normal and MCI classes. Results demonstrate that this approach can differentiate MCI condition with high accuracy and specificity of 81.3% and 82.7%, respectively. The KDE-based features perform better when compared with GLCM features for distinguishing MCI. Conclusions: The KDE-based texture features are able to capture the subtle changes occurring in CC at the MCI stage. This technique achieves comparable performance to other state-of-the-art methods with reduced number of features. Efficiency of the KDE-based texture analysis confirms that the proposed computer assisted technique can be used for mass screening of MCI, which can aid in handling the disease severity.