Kumar Rajamani
Kumar Rajamani
Associate Director, Cropin Technologies
Verified email at - Homepage
Cited by
Cited by
Evaluation of 3D correspondence methods for model building
MA Styner, KT Rajamani, LP Nolte, G Zsemlye, G Székely, CJ Taylor, ...
Information Processing in Medical Imaging: 18th International Conference …, 2003
Statistical deformable bone models for robust 3D surface extrapolation from sparse data
KT Rajamani, MA Styner, H Talib, G Zheng, LP Nolte, MAG Ballester
Medical image analysis 11 (2), 99-109, 2007
Accurate and robust reconstruction of a surface model of the proximal femur from sparse-point data and a dense-point distribution model for surgical navigation
G Zheng, X Dong, KT Rajamani, X Zhang, M Styner, RU Thoranaghatte, ...
IEEE transactions on biomedical engineering 54 (12), 2109-2122, 2007
Bone model morphing for enhanced surgical visualization
KT Rajamani, SC Joshi, MA Styner
2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro …, 2004
Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm
CC Benson, V Deepa, VL Lajish, K Rajamani
2016 International Conference on advances in computing, communications and …, 2016
Brain tumor extraction from MRI brain images using marker based watershed algorithm
CC Benson, VL Lajish, K Rajamani
2015 International Conference on advances in computing, communications and …, 2015
Gaussian process density counting from weak supervision
M von Borstel, M Kandemir, P Schmidt, MK Rao, K Rajamani, ...
Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016
A novel attention-based gated recurrent unit and its efficacy in speech emotion recognition
ST Rajamani, KT Rajamani, A Mallol-Ragolta, S Liu, B Schuller
ICASSP 2021-2021 IEEE international conference on acoustics, speech and …, 2021
Automatic generation of 3D statistical shape models with optimal landmark distributions
T Heimann, I Wolf, HP Meinzer
Methods of Information in Medicine 46 (03), 275-281, 2007
Bone morphing with statistical shape models for enhanced visualization
KT Rajamani, J Hug, LP Nolte, M Styner
Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display …, 2004
Exploring image classification of thyroid ultrasound images using deep learning
KV Sai Sundar, KT Rajamani, S Siva Sankara Sai
Proceedings of the International Conference on ISMAC in Computational Vision …, 2019
A novel and stable approach to anatomical structure morphing for enhanced intraoperative 3D visualization
KT Rajamani, MAG Ballester, LP Nolte, M Styner
Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display …, 2005
Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation
KT Rajamani, H Siebert, MP Heinrich
Journal of Biomedical Informatics 119, 103816, 2021
Use of a dense surface point distribution model in a three-stage anatomical shape reconstruction from sparse information for computer assisted orthopaedic surgery: A …
G Zheng, KT Rajamani, LP Nolte
Asian Conference on Computer Vision, 52-60, 2006
A comparison study assessing the feasibility of ultrasound-initialized deformable bone models
H Talib, K Rajamani, J Kowal, LP Nolte, M Styner, MAG Ballester
Computer Aided Surgery 10 (5-6), 293-299, 2005
Machine learning algorithm for retinal image analysis
R Santhakumar, M Tandur, ER Rajkumar, KS Geetha, G Haritz, ...
2016 IEEE Region 10 Conference (TENCON), 1236-1240, 2016
Real-time vision based driver drowsiness detection using partial least squares analysis
K Selvakumar, J Jerome, K Rajamani, N Shankar
Journal of Signal Processing Systems 85, 263-274, 2016
Multiple instance learning: Robust validation on retinopathy of prematurity
P Rani, R Elagiri Ramalingam, KT Rajamani, M Kandemir, D Singh
Int J Ctrl Theory Appl 9, 451-459, 2016
A novel skull stripping and enhancement algorithm for the improved brain tumor segmentation using mathematical morphology
CC Benson, VL Lajish, K Rajamani
International Journal of Image, Graphics and Signal Processing 8 (7), 59, 2016
Variational Weakly Supervised Gaussian Processes.
M Kandemir, M Haussmann, F Diego, KT Rajamani, J Van Der Laak, ...
BMVC, 71.1-71.12, 2016
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