101
|
Mishra D, Chaudhury S, Sarkar M, Manohar S, Soin AS. Segmentation of Vascular Regions in Ultrasound Images: A Deep Learning Approach. 2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) 2018. [DOI: 10.1109/iscas.2018.8351049] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
102
|
Chaudhary S, Indu S, Chaudhury S. Video‐based road traffic monitoring and prediction using dynamic Bayesian networks. IET INTELLIGENT TRANSPORT SYSTEMS 2018; 12:169-176. [DOI: 10.1049/iet-its.2016.0336] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
103
|
Dang S, Chaudhury S, Lall B, Roy PK. Assessing assumptions of multivariate linear regression framework implemented for directionality analysis of fMRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:2868-71. [PMID: 26736890 DOI: 10.1109/embc.2015.7318990] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Directionality analysis of time-series, recorded from task-activated regions-of-interest (ROIs) during functional Magnetic Resonance Imaging (fMRI), has helped in gaining insights of complex human behavior and human brain functioning. The most widely used standard method of Granger Causality for evaluating directionality employ linear regression modeling of temporal processes. Such a parameter-driven approach rests on various underlying assumptions about the data. The short-comings can arise when misleading conclusions are reached after exploration of data for which the assumptions are getting violated. In this study, we assess assumptions of Multivariate Autoregressive (MAR) framework which is employed for evaluating directionality among fMRI time-series recorded during a Sensory-Motor (SM) task. The fMRI time-series here is an averaged time-series from a user-defined ROI of multiple voxels. The "aim" is to establish a step-by-step procedure using statistical methods in conjunction with graphical methods to seek the validity of MAR models, specifically in the context of directionality analysis of fMRI data which has not been done previously to the best of our knowledge. Here, in our case of SM task (block design paradigm) there is violation of assumptions, indicating the inadequacy of MAR models to find directional interactions among different task-activated regions of brain.
Collapse
|
104
|
Mishra D, Chaudhury S, Sarkar M, Soin AS, Sharma V. Edge Probability and Pixel Relativity-Based Speckle Reducing Anisotropic Diffusion. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2018; 27:649-664. [PMID: 29028196 DOI: 10.1109/tip.2017.2762590] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion-based speckle reducing filter is proposed in this paper. A probability density function of the edges along with pixel relativity information is used to control the diffusion flux flow. The probability density function helps in removing the spurious edges and the pixel relativity reduces the oversmoothing effects. Furthermore, the filtering is performed in superpixel domain to reduce the execution time, wherein a minimum of 15% of the total number of image pixels can be used. For performance evaluation, 31 frames of three synthetic images and 40 real ultrasound images are used. In most of the experiments, the proposed filter shows a better performance as compared to the state-of-the-art filters in terms of the speckle region's signal-to-noise ratio and mean square error. It also shows a comparative performance for figure of merit and structural similarity measure index. Furthermore, in the subjective evaluation, performed by the expert radiologists, the proposed filter's outputs are preferred for the improved contrast and sharpness of the object boundaries. Hence, the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images.
Collapse
|
105
|
Bhugra S, Anupama A, Chaudhury S, Lall B, Chugh A. Multi-modal Image Analysis for Plant Stress Phenotyping. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE 2018:269-280. [DOI: 10.1007/978-981-13-0020-2_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
106
|
Ralekar C, Saha P, Gandhi TK, Chaudhury S. Effect of Devanagari Font Type in Reading Comprehension: An Eye Tracking Study. INTELLIGENT HUMAN COMPUTER INTERACTION 2018:136-147. [DOI: 10.1007/978-3-030-04021-5_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
107
|
Pandey CK, Dash D, Chaudhury S. Impact of Dielectric Pocket on Analog and High-Frequency Performances of Cylindrical Gate-All-Around Tunnel FETs. ECS JOURNAL OF SOLID STATE SCIENCE AND TECHNOLOGY 2018; 7:N59-N66. [DOI: 10.1149/2.0101805jss] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
108
|
Sharma M, Mukhopadhyay R, Chaudhury S, Lall B. An End-to-End Deep Learning Framework for Super-Resolution Based Inpainting. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE 2018:198-208. [DOI: 10.1007/978-981-13-0020-2_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
109
|
Rout DK, Bhat PG, Veerakumar T, Subudhi BN, Chaudhury S. A novel five-frame difference scheme for local change detection in underwater video. 2017 FOURTH INTERNATIONAL CONFERENCE ON IMAGE INFORMATION PROCESSING (ICIIP) 2017. [DOI: 10.1109/iciip.2017.8313727] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
110
|
Ganguly D, Agarwal S, Chaudhury S. Improving Classical OCRs for Brahmic Scripts Using Script Grammar Learning. 2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR) 2017. [DOI: 10.1109/icdar.2017.363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
111
|
Sharma M, Ray A, Chaudhury S, Lall B. A Noise-Resilient Super-Resolution Framework to Boost OCR Performance. 2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR) 2017. [DOI: 10.1109/icdar.2017.83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
112
|
Sharma M, Chaudhury S, Lall B. A Novel Hybrid Kinect-Variety-Based High-Quality Multiview Rendering Scheme for Glass-Free 3D Displays. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2017; 27:2098-2117. [DOI: 10.1109/tcsvt.2016.2564798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
113
|
Srivastava S, Bhugra S, Lall B, Chaudhury S. Drought Stress Classification Using 3D Plant Models. 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) 2017. [DOI: 10.1109/iccvw.2017.240] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
114
|
Goel D, Chaudhury S, Ghosh H. An IoT approach for context-aware smart traffic management using ontology. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE 2017. [DOI: 10.1145/3106426.3106499] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
115
|
Dang S, Chaudhury S, Lall B, Roy PK. Tractography-Based Score for Learning Effective Connectivity From Multimodal Imaging Data Using Dynamic Bayesian Networks. IEEE Trans Biomed Eng 2017; 65:1057-1068. [PMID: 28809668 DOI: 10.1109/tbme.2017.2738035] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition EC is "the causal influence exerted by one neuronal group on another" which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs). METHOD DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data. RESULTS Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments. CONCLUSION EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality. SIGNIFICANCE Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the single modality analysis.
Collapse
|
116
|
Goel D, Chaudhury S, Ghosh H. Multimedia ontology based complementary garment recommendation. 2017 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO WORKSHOPS (ICMEW) 2017. [DOI: 10.1109/icmew.2017.8026317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
117
|
Goel D, Pahal N, Jain P, Chaudhury S. An ontology-driven context aware framework for smart traffic monitoring. 2017 IEEE REGION 10 SYMPOSIUM (TENSYMP) 2017. [DOI: 10.1109/tenconspring.2017.8070059] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
118
|
Dang S, Chaudhury S, Lall B, Roy PK. The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI. J Neurosci Methods 2017; 285:33-44. [PMID: 28495368 DOI: 10.1016/j.jneumeth.2017.05.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 05/05/2017] [Accepted: 05/05/2017] [Indexed: 11/16/2022]
Abstract
BACKGROUND Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. NEW-METHOD High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). RESULTS The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. COMPARISON The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. CONCLUSION Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data.
Collapse
|
119
|
Bhugra S, Anupama A, Chaudhury S, Lall B, Chugh A. Phenotyping of xylem vessels for drought stress analysis in rice. 2017 FIFTEENTH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS (MVA) 2017. [DOI: 10.23919/mva.2017.7986892] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
120
|
Sharma M, Chaudhury S, Lall B. Deep learning based frameworks for image super-resolution and noise-resilient super-resolution. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) 2017. [DOI: 10.1109/ijcnn.2017.7965926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
121
|
Mohan R, Arif M, Wilson J, Chaudhury S, Lall B. Code-Borrowedness of English words in Hindi Language. PROCEEDINGS OF THE FOURTH ACM IKDD CONFERENCES ON DATA SCIENCES 2017. [DOI: 10.1145/3041823.3067693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
122
|
Chaudhury S, Dey L, Verma I, Hassan E. Mining Multimodal Data. PATTERN RECOGNITION AND BIG DATA 2017:581-604. [DOI: 10.1142/9789813144552_0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
123
|
Dang S, Chaudhury S, Lall B, Roy PK. Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data. J Neurosci Methods 2017; 278:87-100. [PMID: 28065836 DOI: 10.1016/j.jneumeth.2016.12.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 12/26/2016] [Accepted: 12/30/2016] [Indexed: 01/14/2023]
Abstract
BACKGROUND Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. NEW METHOD The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. RESULTS The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. CONTROLS The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. COMPARISON The proposed architecture leads to reliable estimates of EC than the existing latent models. CONCLUSIONS This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process.
Collapse
|
124
|
Dokania S, Chopra A, Ahmad F, Indu S, Chaudhury S. Unsupervised Feature Descriptors Based Facial Tracking over Distributed Geospatial Subspaces. LECTURE NOTES IN COMPUTER SCIENCE 2017:196-202. [DOI: 10.1007/978-3-319-69900-4_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
125
|
Sreedevi I, Natarajan J, Chaudhury S. Processing of Historic Inscription Images. DIGITAL HAMPI: PRESERVING INDIAN CULTURAL HERITAGE 2017:245-261. [DOI: 10.1007/978-981-10-5738-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|