GVIP Journal    

GVIP Volume (7), ISSUE (1) ICGST

Dual Tree Complex Wavelet based Regularized Deconvolution for Medical Images

R. Murugesan 1 , V. Thavavel2 and B. Meenakshi Sundaram3

1Department of Physical Chemistry, Madurai Kamaraj University, Madurai - 625021, India
2Department of Applied Sciences, Sethu Institute of Technology, Kariapatti - 626 106, India

Abstract:

Deblurring in the presence of noise is a hard problem, especially in Ultrasound and CT images. In this paper, we propose a hybrid approach of wavelet-based image deconvolution that incorporates Fourier-domain system inversion followed by wavelet-domain noise suppression. In contrast to conventional wavelet-based deconvolution methods, the algorithm employs a regularized inverse filter to operate when the system is non-invertible as well as exploits the properties of dual tree complex wavelet transform (DT-CWT) to remove blur and noise without the need for assuming a specific noise model. Furthermore, the proposed approach uses an adaptive shrinkage function based on median, mean and standard deviation of wavelet coefficients to suppress noise while preserving the sharpness of the image. Its application on ultrasound and CT images has shown a clear improvement over alternative methods.

Keywords: Image Deblurring, Regularized Inverse Filter, Dual Tree Complex Wavelet, and Wavelet denoising.

Author Biography:

Ramachandran Murugesan received his Ph.D Degree from Regional Instrumentation Center, Indian Institute of Technology, Chennai, India. He has been involved in developing novel Electron Magnetic Resonance (EMR) instrumentation such as Flash Photolysis CW EMR, Zero Field EMR spectroscopy etc., during his post doctoral research in US and Australia. With his experience in EMR instrumentation, he is pursuing collaborative research with the Radiation Biology Branch, National Cancer Institute, in the development of pulsed radio frequency FT EMR imaging, Fluorine Electron Double Resonance Imaging (FEDRI) and Overhauser enhanced Magnetic Resonance Imaging (OMRI) techniques for biomedical applications. The imaging technology developments are covered by a number of patents and research publications. His research interests include EMR spectroscopy and imaging, and medical image processing.

 

V. Thavavel received M.C.A and M.Phil Degrees from Madurai Kamaraj University, India. She is an Assistant Professor in Computer Science at Sethu Institute of Technology, Kariapatti, India. She is currently doing her Ph.D., in Computer Science at Madurai Kamaraj University, India. Her research area of interest involves medical image reconstruction in an Object Oriented approach and application of Genetic algorithms to medical image analysis.

 

B. Meenakshi Sundaram received his M.Sc., M.Phil in computer science from Madurai Kamaraj University.  He has served as a Faculty and Head of Academics with an Institution in UAE, affiliated to University of Portsmouth and Scottish Qualifications Authority, UK.  Currently he is working as a Senior Lecturer in Sethu Institute of Technology.  His area of research interest involves semantic context based image retrieval systems, Ontological knowledge base  for medical imaging. 

 

 

BibTex:

@ARTICLE{P1150703002,

AUTHOR = {R. Murugesan  and V. Thavavel and B. Meenakshi Sundaram},

TITLE = {Dual Tree Complex Wavelet based Regularized Deconvolution for Medical Images},

JOURNAL ={ICGST International Journal on Graphics, Vision and Image Processing},

YEAR = {2007},

VOLUME = {07},

ISSUE ={1},

PAGES={1--5}

}

(Full Paper 869KB)