Options
Ramakrishnan Swaminathan
Loading...
Preferred name
Ramakrishnan Swaminathan
Official Name
Ramakrishnan Swaminathan
Alternative Name
Swaminathan, R.
Ramakrishnan, Swaminathan
swaminathan, Ramakrishanan
Sa, Ramakrishnan
Swaminathan, Ramakrishnan
Ramakrishnan, S.
Main Affiliation
Email
ORCID
Scopus Author ID
Google Scholar ID
20 results
Now showing 1 - 10 of 20
- PublicationAutomated Segmentation of Lateral Ventricles in Alzheimer's Conditions Using UNET++ Model(01-01-2022)
;Shaikh, Shabina ;Ganapathy, NagarajanAccurate diagnosis of Alzheimer's disease (AD) in early stage can control the disease progression. Enlargement of Lateral Ventricles (LV) is one of the significant imaging biomarkers for the differentiation of Alzheimer's conditions. However, segmentation of accurate LV for analysis is still challenging. In this work, an attempt is made to segment LV regions from brain MR images using the UNet++ model. For this, axial scans of the MR images are taken from the publicly available Open Access Series of Imaging Studies (OASIS) Brain dataset. LV-based region of interest is segmented using the UNet++ network. Results show that the proposed approach is able to segment brain regions in Alzheimer's conditions. The UNet++ network model yields the highest dice score of 99.4% and sensitivity of 99.3% in segmenting the LV brain region. Thus, the proposed method could be useful for characterizing Alzheimer's condition. - PublicationFractal Order Poro-elastic Model for Modelling Biphasic Tissue and Tissue-Like Materials(01-01-2021)
;Banerjee, Shib Sundar ;Arunachalakasi, ArockiarajanBiological tissues and biopolymeric gels are considered as saturated biphasic structures. Diffusion of interstitial fluid through porous space is a key phenomenon for any biphasic system. Recent studies demonstrated deviation from ideal Fickian diffusion in various complexly structured porous media and this anomaly has been attributed to the fractal topology of the pores. In this study, an attempt has been made to reformulate the standard finite poro-elastic model for a fractal porous media. For this purpose, a fractal order Darcy’s law has been imposed and an appropriate u-p formulation is obtained. Numerical simulation schemes have been developed for two different confined compression scenarios. The results of simulations show that, with ramp and hold protocol, the transient response of the proposed model is influenced by the fractal order but the equilibrium values coincide with the response of an integer-order model. Creep compliance is observed to be inversely proportional to the fractal order of diffusion, which is a consequence of increased drainage rate with a lower order gradient of hydraulic head. A comparison with existing poro-hyperelastic model shows that the proposed fractal poro-hyperelastic model has an increased sensitivity to capture the deformation rate effects. The model has been validated against the results reported in earlier studies with the aid of appropriate model fitting techniques. The proposed model might be useful in modelling biphasic materials in various domains where a hierarchical structure of pore space is apparent, such as tissue ensembles and polymeric gels. - PublicationClassification of biceps brachii muscle fatigue condition using phase space network features(16-06-2020)
;Makaram, NavaneethakrishnaIn this, study, an attempt is made to differentiate muscle nonfatigue and fatigue condition using signal complexity metrics derived from phase space network features. A total of 55 healthy adult volunteers performed dynamic contraction of the biceps brachii muscle. The first and last curl are segmented and are considered as nonfatigue and fatigue condition respectively. A weighted phase space network is constructed and reduced to a binary network based on various radii. The mean and median degree centrality features are extracted from these networks and are used for classification. The results of the classification indicate that these features are capable of differentiating nonfatigue and fatigue condition with 91% accuracy. This method of analysis can be extended to applications such as diagnosis of neuromuscular disorder where fatigue is a symptom. - PublicationProposal of a machine learning approach to differentiate mild and Alzheimer's condition in MR images using shape changes in corpus callosum(01-01-2019)
;Dadsena, Ravi ;Rohini, P.The brain ventricles are surrounded by periventricular structures that are affected by dementia which results in neurodegenerative disorder such as Alzheimer's Disease (AD). The change in morphology of these structures must effect the shape and volume of Corpus Callosum (CC). These alterations in morphology of CC are considered to be a significant image biomarker for the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) subjects. Shape descriptors provide useful information about change in morphology of various brain structures during disease progression. In this work, Lattice Boltzmann criterion based hybrid level set method (LSM) is used to segment CC. Geometric and pseudo-Zernike moment measures are extracted from the segmented area of CC and are statistically analyzed using Statistical Package for Social Science (SPSS). The performance metric of significant moments is validated using machine learning algorithms. Results demonstrate that, hybrid level set is able to delineate CC and the segmented images are in high correlation with ground truth images. High accuracy value of 85.0% has been achieved using Multilayer Perceptron (MLP) classifier for Healthy Control (HC) versus AD subjects. Thus, moments are able to classify MCI from HC and AD subjects with high accuracy and hence the results are found to be clinically significant. - PublicationAnalysis of vasculature in human retinal images using particle swarm optimization based tsallis multi-level thresholding and similarity measures(31-12-2012)
;Raja, Nadaradjane Sri Madhava ;Kavitha, GanesanRetinal vasculature of the human circulatory system which can be visualized directly provides a number of systemic conditions and can be diagnosed by the detection of lesions. Changes in these structures are found to be correlated with pathological conditions and provide information on severity or state of various diseases. In this work, particle swarm optimization algorithm based multilevel thresholding is adopted for detecting the vasculature structures in retinal fundus images. Initially, adaptive histogram equalization is used for pre-processing of the original images. Tsallis multilevel thresholding is used for the segmentation of the blood vessels. Further, similarity measures are used to quantify the similarity between the segmented result and the corresponding ground truth. The optimal multi-threshold selection using particle swarm optimization seems to provide better results. Similarity measures analysis using dendrogram and box plot provide validation of the segmentation procedure attempted. © 2012 Springer-Verlag. - PublicationExplainable Optimized LightGBM Based Differentiation of Mild Cognitive Impairment Using MR Radiomic Features(01-01-2022)
;Shaji, Sreelakshmi ;Palanisamy, RohiniIn this study, an explainable Bayesian Optimized (BO) LightGBM model is employed to differentiate the Corpus Callosal (CC) image features of Healthy Controls (HC) and Mild Cognitive Impairment (MCI). For this, Magnetic Resonance (MR) brain images obtained from a public database are pre-processed and CC is segmented using spatial fuzzy clustering-based level set. Radiomic features are extracted from the segmented CC, which are further fed to BO-LightGBM classifier. SHapley Additive exPlanations (SHAP) technique is used to evaluate the interpretability of the model. The results indicate that radiomics based BO-LightGBM is able to differentiate MCI from HC. An area under curve of 0.83 is achieved by the model. SHAP values suggest that out of 56 radiomic features, texture descriptors possess the highest discriminative power in MCI diagnosis. The performance of adopted approach indicates that radiomics based BO-LightGBM aid in the automated diagnosis of early Alzheimer's Disease stages. - PublicationA binary bat approach for identification of fatigue condition from sEMG signals(01-01-2015)
;Makaram, NavaneethakrishnaIn this work, an attempt has been made to investigate the effectiveness of binary bat algorithm as a feature selection method to classify sEMG signals under fatigue and nonfatigue conditions. The sEMG signals are recorded from the biceps brachii muscle of 50 healthy volunteers. The signals are preprocessed and then multiscale Renyi entropy based feature are extracted. The binary bat algorithm is used for feature selection and the effectiveness is compared with information gain based ranker. The performance of the feature selection algorithms are validated by performing classification using Naïve Bayes, and least square support vector machines. The results show a decreasing trend in the multiscale Renyi entropy with increase in scale. Additionally, higher entropy values where observed in fatigue condition. The classification results showed that a maximum accuracy of 86.66% is obtained with least square SVM and binary bat algorithm. It appears that, this technique is useful in identifying muscle fatigue in varied clinical conditions. - PublicationClassification of tuberculosis digital images using hybrid evolutionary extreme learning machines(17-12-2012)
;Priya, Ebenezer ;Srinivasan, SubramanianIn this work, classification of Tuberculosis (TB) digital images has been attempted using active contour method and Differential Evolution based Extreme Learning Machines (DE-ELM). The sputum smear positive and negative images (N=100) recorded under standard image acquisition protocol are subjected to segmentation using level set formulation of active contour method. Moment features are extracted from the segmented images using Hu's and Zernike method. Further, the most significant moment features derived using Principal Component Analysis and Kernel Principal Component Analysis (KPCA) are subjected to classification using DE-ELM. Results show that the segmentation method identifies the bacilli retaining their shape in-spite of artifacts present in the images. It is also observed that with the KPCA derived significant features, DE-ELM performed with higher accuracy and faster learning speed in classifying the images. © 2012 Springer-Verlag. - PublicationIdentification of EMCI in MR brainstem structure using fractal measures and random forest approach(16-06-2020)
;Palanisamy, RohiniIn this work, an attempt has been made to assess the texture variations of brainstem occurring in the Early Mild Cognitive Impairment (EMCI) condition. Fractal dimension is calculated for the segmented brainstem volume using Higuchi's and Detrending moving average (DMA) method. These measures are validated using Random forest classifier. DMA shows better performance when compared to Higuchi's method in categorising EMCI stage. Thus, the proposed approach using brainstem image's DMA fractal measures with random forest can be used in diagnosis of EMCI. - PublicationDifferentiation of Cell Painted Organelles Using Non Local Texture Descriptor and Random Forest Approach(25-05-2022)
;Palliyil Sreekumar, Sreelekshmi ;Palanisamy, RohiniDiscriminating the cell organelles from microscopic images is a challenging task due to their high similarity in image appearance. In this work, an attempt has been made to differentiate nuclei, Endoplasmic Reticulum (ER) and cytoplasm using a texture pattern descriptor and Random Forest classifier. For this, Cell Painted public dataset from Broad Bioimage Benchmark collection are considered. Texture features are extracted from each image using Non Local Binary Pattern (NLBP) that captures the relationship between global pixels and sampling instances in a local neighborhood. Non local central pixels called anchors are derived from central pixels of image patches and compared with sampling instances. Binary string generated from this is encoded into 29 patterns. Statistical one-way analysis of variance (ANOVA) is performed to select significant features and are validated using Random Forest classifier. The dependency of classifier performance on the local patch radius (R) and the number of anchors (K) are also evaluated. The results indicate that 8 patterns out of 29 are showing strong inter class variability with high F value. Classification accuracy of 84% is achieved with R=3 and K=5. Experimental results demonstrate that the proposed work captures complex patterns in cell structure useful for differentiating cell components which can be employed for evaluating the cytotoxic effects in cell lines.