V. P. Subramanyam Rallabandi
Other affiliations: Delhi Technological University
Bio: V. P. Subramanyam Rallabandi is an academic researcher from National Brain Research Centre. The author has contributed to research in topics: Image texture & Image retrieval. The author has an hindex of 10, co-authored 17 publications receiving 358 citations. Previous affiliations of V. P. Subramanyam Rallabandi include Delhi Technological University.
TL;DR: The proposed stochastic resonance (SR)-based transform in Fourier space for the enhancement of magnetic resonance images of brain lesions can restore the original image from noisy image and optimally enhance the edges or boundaries of the tissues, and enables improved diagnosis over conventional methods.
Abstract: Objective In general, low-field MRI scanners such as the 0.5- and 1-T ones produce images that are poor in quality. The motivation of this study was to lessen the noise and enhance the signal such that the image quality is improved. Here, we propose a new approach using stochastic resonance (SR)-based transform in Fourier space for the enhancement of magnetic resonance images of brain lesions, by utilizing an optimized level of Gaussian fluctuation that maximizes signal-to-noise ratio (SNR). Materials and Methods We acquired the T1-weighted MR image of the brain in DICOM format. We processed the original MR image using the proposed SR procedure. We then tested our approach on about 60 patients of different age groups with different lesions, such as arteriovenous malformation, benign lesion and malignant tumor, and illustrated the image enhancement by using just-noticeable difference visually as well as by utilizing the relative enhancement factor quantitatively. Results Our method can restore the original image from noisy image and optimally enhance the edges or boundaries of the tissues, clarify indistinct structural brain lesions without producing ringing artifacts, as well as delineate the edematous area, active tumor zone, lesion heterogeneity or morphology, and vascular abnormality. The proposed technique improves the enhancement factor better than the conventional techniques like the Wiener- and wavelet-based procedures. Conclusions The proposed method can readily enhance the image fusing a unique constructive interaction of noise and signal, and enables improved diagnosis over conventional methods. The approach well illustrates the novel potential of using a small amount of Gaussian noise to improve the image quality.
TL;DR: Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.
Abstract: The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely compromises the usefulness of network analysis to study the aging brain. We successfully circumvented this problem by focusing on the critical structural network backbone, using a robust tree representation. Whole-brain networks' minimum spanning trees were determined in a dataset of diffusion-weighted images from 382 healthy subjects, ranging in age from 20.2 to 86.2 years. Tree-based metrics were compared with classical network metrics. In contrast to the tree-based metrics, classical metrics were highly influenced by age-related changes in network density. Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.
TL;DR: A technique using stochastic resonance (SR)-based wavelet transform for the enhancement of unclear diagnostic ultrasound images that enhances the edges more clearly and can also optimally enhance an image even if the image noise level is considerable.
Abstract: Ultrasound diagnostic imaging technique is used to visualize muscles and internal organs, their size, structures and possible pathologies or lesions. The limited soft tissue contrast of ultrasound may lead to problems in characterizing perivascular soft tissues. We develop a technique using stochastic resonance (SR)-based wavelet transform for the enhancement of unclear diagnostic ultrasound images. The proposed method enhances the edges more clearly. The advantages of this method are that it can simultaneously operate both as an enhancement process as well as a noise-reduction operation, and that the method can also optimally enhance an image even if the image noise level is considerable.
07 Jun 2016
TL;DR: In this paper, the authors investigated the relationship between microstructural white matter (WM) diffusivity indices and macrostructural WM volume (WMV) among healthy individuals (20-85 years).
Abstract: The aim is to investigate the relationship between microstructural white matter (WM) diffusivity indices and macrostructural WM volume (WMV) among healthy individuals (20–85 years). Whole-brain diffusion measures were calculated from diffusion tensor imaging using FMRIB software library while WMV was estimated through voxel-based morphometry, and voxel-based analysis was carried out using tract-based spatial statistics. Our results revealed that mean diffusivity, axial diffusivity, and radial diffusivity had shown good correlation with WMV but not for fractional anisotropy (FA). Voxel-wise tract-based spatial statistics analysis for FA showed a significant decrease in four regions for middle-aged group compared to young-aged group, in 22 regions for old-aged group compared to middle-aged group, and in 26 regions for old-aged group compared to young-aged group (P < 0.05). We found significantly lower WMV, FA, and mean diffusivity values in females than males and inverted-U trend for FA in males. We conclude differential age- and gender-related changes for structural WMV and WM diffusion indices.
TL;DR: An automated machine learning method using a radial basis function kernel showed good accuracy in classifying different stages of dementia, and is useful for radiological imaging tasks such as diagnosis, prognosis, risk assessment, and early detection.
Abstract: Background and objective Early detection of dementia for clinical diagnosis is challenging due to high subjectivity and individual variability in cognitive assessments, as well as the evaluation of protein biomarkers, which are mostly used for staging of Alzheimer's disease. Currently, although there is no effective treatment for Alzheimer's disease, early detection of dementia through magnetic resonance imaging analysis may assist in developing preventive measures to slow disease progression. In this paper, we developed an automated machine learning method for classifying cognitively normal aging, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease individuals. Materials and methods In this study, a total of 1167 whole-brain magnetic resonance imaging scans of individuals who are cognitively normal aging controls, early mild cognitive impairment, late mild cognitive impairment, and patients with probable Alzheimer's disease were obtained from the Alzheimer's Disease Neuroimaging Initiative database. We measured regional cortical thickness of both left and right hemispheres (68 features) using FreeSurfer analysis for each individual, and utilized these 68 features for model building. We further tested scans of individuals to classify them into four groups using various machine learning methods. Results We found that the cortical thickness feature, based on the non-linear support vector machine classifier with radial basis function, showed the highest specificity (0.77), sensitivity (0.75), F-score (0.72), Matthew's correlation coefficient (0.71), Kappa-statistic (0.69), receiver operating characteristic area under the curve (0.76), and an overall accuracy of 75% in classifying all four groups using ten-fold cross-validation with respect to the clinical scale. In addition, we also predicted the features for classifying all four groups using the support vector regression algorithm. Conclusion The non-linear support vector machine using a radial basis function kernel showed good accuracy in classifying different stages of dementia. Thus, machine learning methods are useful for radiological imaging tasks such as diagnosis, prognosis, risk assessment, and early detection.
TL;DR: Recent advances in the understanding of how age affects the authors' brain's intrinsic organization are discus, and a perspective on potential challenges and future directions of the field is shared.
Abstract: Over the past decade there has been an enormous rise in the application of functional and structural connectivity approaches to explore the brain's intrinsic organization in healthy and clinical populations. The notion underlying the application of these approaches to study aging is that subtle age-related disruption of the brain's regional integrity and information flow across the brain, are expressed by age-related differences in functional and structural connectivity. In this review I will discus recent advances in our understanding of how age affects our brain's intrinsic organization, and I will share my perspective on potential challenges and future directions of the field.
01 Jan 2005
TL;DR: In this paper, the authors used Monte Carlo simulations to investigate the optimal value of the diffusion weighting factor b for estimating white-matter fiber orientations using diffusion MRI with a standard spherical sampling scheme.
Abstract: This study uses Monte Carlo simulations to investigate the optimal value of the diffusion weighting factor b for estimating white-matter fiber orientations using diffusion MRI with a standard spherical sampling scheme. We devise an algorithm for determining the optimal echo time, pulse width, and pulse separation in the pulsed-gradient spin-echo sequence for a specific value of b. The Monte Carlo simulations provide an estimate of the optimal value of b for recovering one and two fiber orientations. We show that the optimum is largely independent of the noise level in the measurements and the number of gradient directions and that the optimum depends only weakly on the diffusion anisotropy, the maximum gradient strength, and the spin-spin relaxation time. The optimum depends strongly on the mean diffusivity. In brain tissue, the optima we estimate are in the ranges [0.7, 1.0] x 10(9) s m(-2) and [2.2, 2.8] x 10(9) s m(-2) for the one- and two-fiber cases, respectively. The best b for estimating the fractional anisotropy is slightly higher than for estimating fiber directions in the one-fiber case and slightly lower in the two-fiber case. To estimate Tr(D) in the one-fiber case, the optimal setting is higher still. Simulations suggest that a ratio of high to low b measurements of 5 to 1 is a good compromise for measuring fiber directions and size and shape indices.
TL;DR: A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented, which differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image.
Abstract: A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented in this paper. The local region of image is represented by local maximum edge binary patterns (LMEBP), which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further, the effectiveness of our algorithm is confirmed by combining it with Gabor transform. Four experiments have been carried out for proving the worth of our algorithm. Out of which three are meant for CBIR and one for object tracking. It is further mentioned that the database considered for first three experiments are Brodatz texture database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and the fourth contains three observations. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.
TL;DR: A content-based image retrieval (CBIR) method for diagnosis aid in medical fields, where images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform, which characterize the distribution of wavelet coefficients in each subband of the decomposition.
Abstract: We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme Weights are also introduced between subbands All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database Results are promising: a mean precision of 5650%, 7091% and 9610% is achieved for these three databases, when five images are returned by the system
TL;DR: This study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization.
Abstract: The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.