S
Sudeb Das
Researcher at Indian Statistical Institute
Publications - 23
Citations - 793
Sudeb Das is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Contourlet & Image fusion. The author has an hindex of 12, co-authored 23 publications receiving 713 citations. Previous affiliations of Sudeb Das include Tata Consultancy Services.
Papers
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Journal ArticleDOI
NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency
Sudeb Das,Malay K. Kundu +1 more
TL;DR: A novel multimodal medical image fusion (MIF) method based on non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN) is presented, which exploits the advantages of both the NSCT and the PCNN to obtain better fusion results.
Journal ArticleDOI
A Neuro-Fuzzy Approach for Medical Image Fusion
Sudeb Das,Malay K. Kundu +1 more
TL;DR: This paper addresses a novel approach to the multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of the nonsubsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN).
Journal ArticleDOI
Brain Mr Image Classification Using Multiscale Geometric Analysis of Ripplet
TL;DR: Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is e-cient in brain MR image classiflcation.
Journal ArticleDOI
Effective management of medical information through ROI-lossless fragile image watermarking technique
Sudeb Das,Malay K. Kundu +1 more
TL;DR: The proposed scheme combines lossless data compression and encryption technique to embed electronic health record (EHR)/DICOM metadata, image hash, indexing keyword, doctor identification code and tamper localization information in the medical images.
Proceedings ArticleDOI
Lossless ROI Medical Image Watermarking Technique with Enhanced Security and High Payload Embedding
Malay K. Kundu,Sudeb Das +1 more
TL;DR: The effectiveness of the proposed scheme, proven through experiments on various medical images through various image quality measure matrices enables it to be argued that, the method will help to maintain Electronic Patient Report (EPR)/DICOM data privacy and medical image integrity.