Dr. Deep Gupta >>Home

DG-3Deep Gupta, Ph.D.

Assistant Professor
Department of Electronics and Communication Engineering
Visvesvaraya National Institute of Technology, Nagpur-440010
Maharashtra, INDIA
 
Email: deepgupta[at]ece[dot]vnit[dot]ac[dot]in, er.deepgupta[at]gmail[dot]com
Contact: +91 712 280 1855, +91 9358190782
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Current Opening: Rolling Advertisement: 01 junior research fellow (JRF) for SERB-funded research project (For more details: click here)


Biosketch

Deep Gupta is a researcher in image processing, computer vision, medical image analysis, and artificial intelligence. He is an Assistant Professor in the Department of Electronics and Communication Engineering at Visvesvaraya National Institute of Technology (VNIT) Nagpur, Maharashtra, lab In-charge of Centre for Artificial Intelligence (CAI), Biomedical image analysis, Senior Member, IEEE, and life member of the Ultrasonic Society of India. His research interests include machine learning, image analysis, computer vision, and multimodal analysis in healthcare. In 2015-16, he was an assistant professor in electronics and communication engineering at the Thapar University Patiala. From 2011 to 2015, he was a Doctoral fellow in the Biomedical Lab, Department of Electrical Engineering at Indian Institute of Technology Roorkee (IITR), India. In 2010, he received a two-year Master's degree (M.Tech.) in Electrical Engineering from the Indian Institute of Technology Roorkee, and four-year Bachelor’s degree (B.Tech.) in Electronics and Communication Engineering from the Uttar Pradesh Technical University Lucknow in 2005.


Research Interests

My research interests are in the field of

  • Image processing and computer vision
  • Medical image analysis
  • Multimodal image registration and fusion
  • Histopathological image analysis
  • Food image analysis by end-to-end learning
  • Video segmentation and analysis
  • Image segmentation
  • Multiclass classification
  • Event detection
  • Cognitive analysis

Educational Details

  • July 2011 – June 2015 | Doctor of Philosophy in the area of Ultrasound Medical Image Analysis from Indian Institute of Technology Roorkee, India
  • July 2008 – June 2010 | Master of Technology in System and Control from Indian Institute of Technology Roorkee, India
  • July 2001 – June 2005 | Bachelor of Technology in Electronics and Communication Engineering from, MIT Moradabad, Uttar Pradesh Technical University Lucknow, India

Professional Details

  • June 2016 – Present     | Assistant Professor | Visvesvaraya National Institute of Technology Nagpur, India
  • July 2015 – June 2016  | Assistant Professor | Thapar University Patiala, India
  • July 2010 – June 2011  | Assistant Professor | Moradabad Institute of Technology Moradabad, India   
  • Aug 2005 – June 2008  | Assistant Professor | Moradabad Institute of Technology Moradabad, India   

Awards/Grants

  • 2022 | Core Research Grant of INR 24 lacs from Science and Engineering Research Board India
  • 2019 | International travel grant received from Science and Engineering Research Board India
  • 2017 | Seal of Excellence received from  European Commission in Horizon 2020
  • 2016 | Dr. T.K. Saksena Memorial Award received from Ultrasound by Ultrasonic Society of India
  • 2014 | Dr. S. Parthasarathy Award 2014 by Ultrasonic Society of India

Membership

  1. Senior Member, IEEE, Membership number: 94230310, since February 16, 2019
  2. Life Member, Ultrasonic Society of India (USI), Membership number: LM 340, since July 01, 2017
  3. Life Member, International Association of Engineers (IAENG), Membership number: 110884, since 10 January 2011
  4. Life Member, International Association of Computer Science and Information Technology (IACSIT), Membership number: 80337838, since 06 May 2010 

Research Projects 

Project Title: Development of fully-automated liver cancer detection and severity estimation system from H&E stained histopathological images
Funding Agency: CRG, SERB
Role: Principal Investigator
Duration: 2022-2025
Sanctioned Amount: INR 24.62 Lacs
Other Investigators: Ankit Bhurane, Dr. Nisha Meshram, MD, AIIMS Nagpur
Project Title: Low-cost real-time dual-mode photoacoustic ultrasound imaging technology for non-invasive diagnosis and management of cancer
Funding Agency: Scheme for Promotion of Academic and Research Collaboration
Role: Co-Principal Investigator
Duration: 2019-2021
Sanctioned Amount: INR 45.27 Lacs
Other Investigators:  Saugata Sinha (PI), VR Satpute, K.M. Bhurchandi, A.S. Gandhi

Selected Research Publications 

Referred Journal Publications

  1. Bhagyashree V Lad, Manisha Das, Mohammad Farukh Hashmi, Avinash G Keskar, Deep Gupta, “Saliency detection using a bio-inspired spiking neural network driven by local and global saliency,” Applied Artificial Intelligence, vol. 36, no. 1, 2022. (Impact factor: 2.8)

  2. Manisha Das, Deep Gupta, Petia Radeva, Ashwini M. Bakde, Optimized multimodal neurological image fusion based on low-rank texture prior decomposition and super-pixel segmentationIEEE Transactions on Instrumentation & Measurement, vol. 71, pp 1-9, 2022. (Impact factor: 5.6)

  3. Manisha Das, Deep Gupta, Petia Radeva, Ashwini M. Bakde, Multimodal image sensor fusion in a cascaded framework using optimized dual channel pulse coupled neural networkJournal of Ambient Intelligence and Humanized Computing, pp. 1-20, 2022. (Impact factor: 3.662)

  4. Ankush D Jamthikar, Deep Gupta, Laura E. Mantella, Luca Saba, Amer M.Johri, J.S.Suri, Ensemble machine learning and its validation for prediction of coronary artery disease and acute coronary syndrome using focused carotid ultrasoundIEEE Transactions on Instrumentation and Measurement, vol. 71, 1-10, 2022. (Impact factor: 5.6)

  5. Ankush D Jamthikar, Deep Gupta, Amer M.Johri, Laura E. Mantella, Luca Saba, J.S.Suri, A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian studyComputers in Biology and Medicine, vol. 140, 2022. (Impact factor: 7.7)

  6. Manisha Das, Deep Gupta, Petia Radeva, Ashwini M. Bakde, Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid l1-l0 layer decomposition domainBiomedical Signal Processing and Control, vol. 68, 2021. (Impact factor: 5.1)

  7. Sneha Singh and Deep Gupta, Detail enhanced feature-level medical image fusion in decorrelating decomposition domain, IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9, 2021, (Impact factor: 5.6) doi: 10.1109/TIM.2020.3038603. 

  8. Manisha Das, Deep Gupta, Petia Radeva, Ashwini M. Bakde, NSST domain CT-MR neurological image fusion using optimized biologically inspired neural networkIET Image Processing, vol 14, no. 16, pp. 4291-4305, 2021. (Impact factor: 2.3)

  9. Manisha Das, Deep Gupta, Petia Radeva, Ashwini M. Bakde, Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimizationInternational Journal of Imaging Systems and Technology, 2021. (Impact factor: 3.3)

  10. Sneha Singh and Deep Gupta, Multistage multimodal medical image fusion model using feature adaptive pulse coupled neural network, International Journal of Imaging Systems and Technology, vol. 31, no. 2, pp. 981-1001, 2021. (Impact factor: 3.3)

  11. Ankush D Jamthikar, Deep Gupta, L.E. Mantella et al., Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants studyThe International Journal of Cardiovascular Imaging, vol 37, no. 4, pp. 1171-1187, 2021. (Impact factor: 2.357)

  12. Ankush D Jamthikar, Deep Gupta, A. M. Johri, et al., Low-cost office-based cardiovascular risk strati cation using machine learning and focused carotid ultrasound in an Asian-Indian cohortJournal of Medical Systems, vol. 44, no. 12, pp. 1-15, 2020. (Impact factor: 5.3)

  13. Ankush D Jamthikar, A, Puvvula Deep Gupta, AM Johri, et al., Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review, International Angiology: a Journal of the International Union of Angiology, vol. 40, no. 2, pp. 150-164, 2020. (Impact factor: 2.789)

  14. V. Viswanathan, Ankush D Jamthikar, Deep Gupta, A. Puvvula, et al., Does the carotid bulb o er a better 10-Year CVD/Stroke risk assessment compared to the common carotid artery? A 1516 ultrasound scan studyAngiology, vol. 71, no.10, pp. 920-933, 2020. (Impact factor: 3.619)

  15. Ankush D Jamthikar, Deep Gupta, L. Saba, N. N. Khanna, et al., Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasoundComputers in Biology and Medicine, vol. 126, pp.1-20, 2020. (Impact factor: 7.7)

  16. Ankush D Jamthikar, Deep Gupta, A. Puvvula, A. M. Johri, N. N. Khanna, L. Saba, et al., Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imagingRheumatology International, vol. 40, pp.1921-1939, 2020. (Impact factor: 2.631)

  17. Ankush D Jamthikar, Deep Gupta, N. N. Khanna, L. Saba, J. R. Laird, and J. S. Suri, Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factorsIndian Heart Journal, vol. 72, no. 4, pp. 258-264, 2020.

  18. V. Viswanathan, Ankush D Jamthikar, Deep Gupta, et al., Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease, International Angiology: A Journal of the International Union of Angiology, vol. 39, pp. 290-306, 2020. (Impact factor: 2.789)

  19. Ankush D Jamthikar, Deep Gupta, E. Cuadrado-Godia, A. Puvvula, N. N. Khanna, L. Saba, et al., Ultrasound-based stroke/cardiovascular risk strati cation using Framingham Risk Score and ASCVD Risk Score based on Integrated Vascular Age" instead of Chronological Age: a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts, Cardiovascular Diagnosis and Therapy, vol. 10, no. 4, pp. 939-954, 2020. (Impact factor: 2.845)

  20. Ankush D Jamthikar, Deep Gupta, L. Saba, et al., Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models, Cardiovascular Diagnosis and Therapy, vol. 10, pp. 919-938, 2020. (Impact factor: 2.845)

  21. A Puvvula, Ankush D Jamthikar, Deep Gupta, et al., Morphological carotid plaque area is associated with glomerular filtration rate: A study of south Asian Indian patients with diabetes and chronic kidney disease, Journal of Angiology, vol. 71, no. 6, pp. 520-535, 2020. (Impact factor: 3.619)

  22. V. Viswanathan, Ankush D Jamthikar, Deep Gupta, A. Puvvula, N. N. Khanna, L. Saba, et al., Integration of eGFR biomarker in image-based CV/Stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney diseaseInternational Angiology: A Journal of the International Union of Angiology, vol. 39, no. 4, pp. 290-306, 2020. (Impact factor: 2.789)

  23. Ankush D Jamthikar, Deep Gupta, J.S. Suri, et al., Low-cost preventive screening using carotid ultrasound in patients with diabetesFrontiers in Bioscience, vol. 25, pp. 1131-1171, 2020. (Impact factor: 2.747)

  24. Ankush D Jamthikar, Deep Gupta, Khanna NN, et al., A special report on changing trends in preventive stroke/cardiovascular risk assessment via B-mode ultrasonographyCurrent Atherosclerosis Reports, vol. 21, no. 7, pp. 1-16, 2019. (Impact factor: 5.113)

  25. Ankush D Jamthikar, Deep Gupta, et al., A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes, Cardiovascular Diagnosis and Therapy, vol. 9, no. 5, pp. 420-430, 2019. (Impact factor: 2.845)

  26. NN Khanna, Ankush D Jamthikar, Deep Gupta, Araki T et al., Effect of Carotid Image-based Phenotypes on Cardiovascular Risk Calculator: AECRS1.0, Medical & Biological Engineering & Computing, vol. 57, no. 7, pp. 1553-1566, 2019. (Impact factor: 2.602)

  27. E Cuadrado-Godia , Ankush D Jamthikar, Deep Gupta, NN Khanna, et al., Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approachComputers in Biology and Medicine, vol.108, pp. 182-195, 2019. (Impact factor: 7.7)

  28. NN Khanna, Ankush D Jamthikar, Deep Gupta, M Piga, et al., Rheumatoid arthritis: atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning-based tissue characterizationCurrent Atherosclerosis Reports, vol. 21, no. 2, pp. 1-14, 2019. (Impact factor: 5.113)

  29. NN Khanna, AD Jamthikar, Deep Gupta, et al., Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic studyComputers in Biology and Medicine, vol. 105, pp.125-143, 2019. (Impact factor: 7.7)

  30. Luca Saba AD Jamthikar, L Saba, Deep Gupta, J.S. Suri, et al., Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited? International Angiology, vol. 38, no. 6, pp. 451-465, 2019. (Impact factor: 2.789)

  31. NN Khanna, AD Jamthikar, Deep Gupta, et al., Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort studyEchocardiography, vol. 36, no.2, pp. 345-361, 2019. (Impact factor: 1.724)

  32. SM Nemalidinne, Deep Gupta, Nonsubsampled contourlet domain visible and infrared image fusion framework for re detection using pulse coupled neural network and spatial fuzzy clusteringFire Safety Journal, vol. 101, pp. 84-101, 2018. (Impact factor:2.764)

  33. V Kotsis, Ankush D Jamthikar, T Araki, Deep Gupta, J. R. Laird, et. al., Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients, Diabetes Research and Clinical Practice, vol. 143, pp. 322-331, 2018. (Impact factor: 5.602)

  34. A Boi, Ankush D Jamthikar, L Saba, Deep Gupta, et al., A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography, Current Atherosclerosis Reports, vol. 20:30, pp. 1-17, 2018. (Impact factor: 5.113)

  35. Deep Gupta, Nonsubsampled shearlet domain fusion techniques for CT-MR neurological images using improved biological inspired neural model, Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 262-274, 2018. (Impact factor: 4.314)

  36. S. Singh, RS. Anand, Deep Gupta, CT and MR image information fusion scheme using a cascaded framework in ripplet and nonsubsampled shearlet domain ModelIET Image Processing, vol. 12, pp. 696-707, 2018. (Impact factor: 2.3)

  37. Shilpa Suresh, D. Das, S. Lal, Deep Gupta, Image quality restoration framework for contrast enhancement of satellite remote sensing imagesRemote Sensing Applications: Society and Environment, vol. 10, pp. 104-119, 2018

  38. Deep Gupta and R.S. Anand, A hybrid edge-based segmentation approach for ultrasound medical images, Biomedical Signal Processing and Control, vol. 31, pp. 116-126, 2016. (Impact factor: 5.1)

  39. Sneha Singh, Deep Gupta, RS Anand, Vinod Kumar, Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network, Biomedical Signal Processing and Control, vol. 18, pp. 91-101, 2015. (Impact factor: 5.1)

  40. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Speckle filtering of ultrasound images using a modified nonlinear diffusion model in nonsubsampled shearlet domainIET Image Processing, vol. 9, no. 2, pp. 107-117, 2015. (Impact factor: 2.3)

  41. Deep Gupta, R.S. Anand, and Barjeev Tyagi, A hybrid segmentation method based on Gaussian kernel fuzzy clustering and region-based active contour model for ultrasound medical imagesBiomedical Signal Processing and Control, vol. 16, pp. 98-112, 2015. (Impact factor: 5.1)

  42. Deep Gupta, R.S. Anand, and BarjeevTyagi, Despeckling of ultrasound medical images using ripplet domain non-linear filtering, Signal, Image and Video Processing, vol. 9, no. 5, pp. 1093-1111, 2015. (Impact factor: 2.157)

  43. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Despeckling of ultrasound medical images using nonlinear adaptive anisotropic diffusion in nonsubsampled shearlet domain, Biomedical Signal Processing and Control, vol. 14, pp. 55-65, 2014. (Impact factor: 5.1)

  44. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Ripplet domain non-linear filtering for speckle reduction in ultrasound medical images, Biomedical Signal Processing and Control, vol. 10, pp. 79-91, 2014. (Impact factor: 5.1)

  45. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Despeckling of ultrasound images of bone fracture using M-band ridgelet transformOptik – International Journal for Light and Electron Optics, vol. 125, no. 3, pp. 1417-1422, 2014. (Impact factor: 2.443)

  46. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Edge preserved enhancement of medical images using adaptive fusion-based denoising by shearlet transform and total variation algorithmJournal of Electronic Imaging, vol. 22 4, 2013. (Impact factor: 0.945)

  47. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Edge preserved enhancement of medical ultrasound images using multiscale ripplet transform-based OGS algorithmJournal of Pure and Applied Ultrasonics, vol. 35, no. 4, pp. 111-119, 2013.


Referred Conference/Seminar Publications 

  1. Pranay Dumbhare, Yash Dubey, Vedant Phuse, Ankush Jamthikar, Himanshu Padole, Deep GuptaCombining Deep-Learned and Hand-Crafted Features for Segmentation, Classification and Counting of Colon Nuclei in H &E Stained Histology Images,” 7th IAPR International Conference on Computer Vision and Image Processing, 2023, pp. 686-698.

  2. Rishesh Agarwal, Manisha Das, Deep Gupta, Petia Radeva, Video Colorization using Modified Autoencoder Generative Adversarial Networks,” 7th IAPR International Conference on Computer Vision and Image Processing (CVIP2022), 2023, pp. 304-315.

  3. Vishwesh Pillai, Pranav Mehar, Manisha Das, Deep Gupta, Petia Radeva, “Integrated hierarchical and flat classifiers for food image classification using epistemic uncertainty,” 14th IEEE International Conference on Signal Processing and Communications (SPCOM2022), IISc Bangalore, July 11-15, 2022.

  4. Dhruvi Shah, Hareshwar Wani, Mansiha Das, Deep Gupta, Petia Radeva, Ashwini M. Bakde STPGANsFusion: structure and texture preserving generative adversarial networks for multimodal medical image fusion, IEEE 28th National Conference on Communications (NCC-2022), IIT Bombay, May 24-27, 2022.

  5. Manisha Das, Deep Gupta, Petia Radeva, Ashwini M. Bakde, Deep and bio-inspired spiking neural networks based optimized multimodal neurological image fusion model, 9th International Conference on Pattern Recognition and Machine Intelligence (PreMI-2021), ISI Kolkata, December 15-18, 2021.

  6. Rohit Lal, Kush Agarwal, Himanshu Patil, Deep Gupta, K Surender, Optimized bio-inspired spiking neural models based anatomical and functional neurological image fusion in NSST domain, IEEE 27th National Conference on Communications (NCC-2021), IIT Kanpur, July 27-30, 2021.

  7. Manisha Das, Deep Gupta, Petia Radeva, Ashwini M. Bakde, DeepSCT: Deep learning-based self-correcting object tracking mechanism, IEEE 27th National Conference on Communications (NCC-2021), IIT Kanpur, July 27-30, 2021.

  8. T.M. Kumar, M.V.N. Maanas Sai, Deep Gupta, Transform domain rain removal methods using dictionary learning approach: A comparative study, in 11th International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 310-315, 2020.

  9. K.V. Reddy, T.C. Reddy, Deep Gupta, Convolutional neural network-based MRI brain tumor identification, in 11th International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 304-309, 2020.

  10. V Saraswathi, Deep Gupta, CNN and RF-based classification of Brain Tumors in MR neurological image, in 4th International Conference Computer vision and Image Processing (CVIP-2019), Sep. 27-29, 2019, MNIT Jaipur, India.

  11. V Saraswathi, Deep Gupta, Classification of brain tumor using PCA-RF in MR neurological images, in 12th IEEE International Conference on Communication Systems & Networks (COMSNETS), pp. 440-443, Jan. 7-11, 2019, Bengaluru, India.

  12. S. Mouni, P. Sindhu Anem, Deep Gupta, Nonsubsampled contourlet domain fusion approach for infrared and visible fire images, in IEEE Region 10 conference TENCON, pp. 2516-2521, Oct. 28-31, 2018, South Korea.

  13. Deep Gupta, R.S. Anand and Barjeev Tyagi, Enhancement of medical ultrasound images using non-linear filtering based on rational-dilation wavelet transform, in International Conference on Signal Processing and Imaging Engineering, Oct. 24-26 2012, San Francisco, USA, pp. 615-620, USA.

  14. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Enhancement of medical ultrasound images using multiscale discrete shearlet transform based thresholding, in IEEE International Symposium of Electronic, System and Design, pp. 286-290, Dec. 19-22, 2012, BESU, Howrah, India.

  15. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Efficient wavelet-based noise removal algorithm for natural images corrupted by Gaussian noise, in IEEE International Conference on aerospace electronics, communication and instrumentation, Jan. 6-7, 2010, VRSEC Vijaywada, India.

  16. Deep Gupta, R.S. Anand, and Barjeev Tyagi, A new method of image denoising based on wavelet transform, in International Conference on engineering innovations- A fllip to economic development, Feb. 18-20, Feb. 2010, CGI Punjab, India.


Book Chapters/Monogram/Technical Reports 

  1. Manisha Das, Deep Gupta, Petia Radeva and Ashwini M Bakde, A swarm optimized hybrid layer decomposition and reconstruction model for multi-modal medical image fusion, Artificial Intelligence Applications for Health Care, Taylor & Francis, CRC Press, pp. 203-225, 2022, eBook ISBN: 9781003241409.

  2. T.C. Ulli, Deep GuptaSegmentation of calcified plaques in intravascular ultrasound images, Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, Springer, pp 57-67, 2020, Online ISBN: 978-981-13-9683-0, Print ISBN: 978-981-13-9682-3.

  3. Ankush D Jamthikar, Vasileios Kotsis, Tadashi Araki, Deep Gupta, et al., Echolucency-based phenotype in carotid atherosclerosis disease for risk strati cation of diabetes patients, Vascular and Intravascular Imaging Trends, Analysis, and Challenges, volume 2, IOP Publishing Aug. 2019. Online ISBN: 978-0-7503-2002-3, Print ISBN: 978-0-7503-1999-7.

  4. Ankush D Jamthikar, Alberto Boi, Luca Saba, Deep Gupta, John R Laird, N N Khanna and Jasjit Suri, Risk of coronary artery disease: genetics and external factors, Vascular and Intravascular Imaging Trends Analysis, and Challenges, volume 2, IOP Publishing Aug. 2019. Online ISBN: 978-0-7503-2002-3, Print ISBN:978-0-7503-1999-7.

  5. Ankush D Jamthikar, Alberto Boi, Luca Saba, Deep Gupta, John R Laird, N N Khanna and Jasjit S Suri, Wall quantification and tissue characterization of the coronary artery, Vascular and Intravascular Imaging Trends, Analysis, and Challenges, volume 2, IOP Publishing Aug. 2019. Online ISBN: 978-0-7503-2002-3, Print ISBN: 978-0-7503-1999-7.

  6. Ankush D Jamthikar, Alberto Boi, Luca Saba, Deep Gupta, John R Laird, N N Khanna and Jasjit S Suri, Rheumatoid arthritis: its link to atherosclerosis imaging and cardiovascular risk assessment using machine-learning-based tissue characterization, Vascular and Intravascular Imaging Trends, Analysis, and Challenges, volume 2, IOP Publishing Aug. 2019. Online ISBN: 978-0-7503-2002-3, Print ISBN: 978-0-7503-1999-7.

  7. Deep Gupta, R.S. Anand, and Barjeev Tyagi, Despeckling of ultrasound images of bone fracture using RADWT based non-linear filtering, Lecture Notes in Electrical Engineering, pp. 697-711, 2013, Springer Netherlands. DOI 10.1007/978-94-007-6818-5 49,  ISBN: 978-94-007-6817-8 Published by Springer.