REAL TIME COMPUTERIZED TOMOGRAPHIC IMAGE & VIDEO DENOISING,
COMPRESSION USING ADVANCED WAVELETS & MULTIWAVELETS AND
TRANSMISSION FOR HEALTH CARE DIAGNOSIS
PRINCIPAL INVESTIGATOR: (Mentor)
Dr.Srinivasan Vathsal
Professor and Head, Dept of EEE, Dean R&D,
JBIET, Yenkapally, Moinabad, RR Dist.
svathsal@gmail.com
07702299775
CO-PRINCIPAL INVESTIGATORS (Mentee)
Dr.Syed Amjad Ali
Professor, Department of ECE
Sy.No:32, Lords Institute of Engineering and Technology,
Himayath sagar, Hyderabad.
Syedamjadali.lords@gmail.com
PROPOSAL SUMMARY
• Real time imaging is the rapid acquisition and
manipulation of information from a scanning
probe by electronic circuits to enable images to
be produced on TV screens almost
instantaneously.
• Medical imaging is used for clinical diagnosis and
computer aided surgery.
• We have chosen medical tomographic imaging
area to denoise computerized tomographic
images and video, visual quality enhancement
using enhancement techniques, compress it and
transmit simultaneously, while real time imaging.
Medical imaging is used for clinical diagnosis and
computer aided surgery.
• We propose to design and develop the algorithms
and techniques in the following areas,
• Identification of noise in the CT imaging (image and
video)
• Noise variance estimation techniques for specific
noise.
• Denoise using advanced wavelet and multiwavelet
techniques
• Quality enhancement technique for image and
video
• Compression using advanced compression
techniques.
• Transmission techniques for transmission of images
and video.
• There is a need for transmission of CT images
in telemedicine for quick diagnosis and
remedial action at very short duration of time.
• Simultaneously transmission of information
helps the medical practioners in taking instant
decision for treatment, as it is concerned to
health of a patient.
• This proposal can be treated as a good project
of national interest for instant help in medical
treatment for life saving under “Mobile-Health
Care”.
TECHNICAL DESCRIPTION
Technical objective:
• This project is proposed in the national interest
for speedy medical treatment for the patient as
and when approaches, as it is related to
precious life of a human being.
• To obtain real time computerized tomographic
imaging, denoising, quality enhancement,
compressing and transmitting the images and
the video to any part of the world for better
medical diagnosis, opinion and the instant
treatment.
Relation to prior work:
• This project is an extension of our Ph.D research work,
wherein, we carried out the research work for denoising
the tomographic images using wavelets[7], [9-11], [16],[17],
[19], [20], [22], [26] and multiwavelet techniques[4], [8],
[13], [27], [28] for decomposition.
• Developed univariate and multivariate thresholding
techniques[3] for denoising.
• Developed a modified visual quality enhancement
techniques of images
i) using modified Canny based edge detection
algorithms[12].
ii)using modified morphological thinning operation.
• Developed lossy and lossless compression techniques[18]
for compression of an image. We have applied the above
techniques for the still and natural MRI images.
• To still images, we have added additive white Gaussian
noise at different noise levels and denoised using window
based multiwavelet transformation[14], [23] and
thresholding techniques.
• We developed genetic algorithm based window
selection[24] for the optimization of windowing technique.
• We have also developed estimation techniques[1], [2], [15],
[21], [29], [30] to estimate the noise content in natural MRI
images of a patient, and then applied denoising techniques
using wavelets, multiwavelets and thresholding.
• As an extension work, we would like to estimate the noise
while real time imaging (image & video) and then apply
advanced denoising techniques using wavelets and
multiwavelet transformations for decomposing and then
thresholding to remove noise, then apply advanced
compression techniques, and use high speed transmission
at fast rate, every thing simultaneously while imaging.
Research challenges
• Design and development of algorithms for identifying the
type of noise in natural image and video.
• Design and development of algorithm for efficient noise
variance estimation techniques while imaging natural
images and video.
• Design and development of algorithms for denoising
image and video using advanced fast wavelet and
multiwavelet transforms for decomposition and
reconstruction.
• Design and development of algorithms for advanced
thresholding techniques.
• Design and development of algorithms for advanced
lossless compression techniques.
• Design and development of algorithms compatible to safe
and fast transmission without loss of data, for the
transmission of huge data in the form of images and videos,
simultaneously while imaging.
The above proposed techniques can be
grouped into two categories:
• Design and development of algorithms for the
identification, estimation, denoising and
compression techniques.
• Design and development of algorithms for
transmission of images and videos.
Innovative aspect of proposed work:
• Every aspect of above proposed algorithm will
be new and innovative work.
• We identify noise in real time image by
comparing the noise with the predefined
noise model.
• Then estimate noise variance by using
methods like Median Absolute Deviation.
• In order to denoise an image, we propose
modified Discrete Wavelet Transform and
modified CL Multiwavelet Transform by
constructing the multifilter coefficients and
implementing in the transformation
techniques.
• For compression, we propose Set Partition in
Heirarchical Tree (SPIHT) and Discrete Cosine
Transform.
• For Quality enhancement of an image, we
propose modified Canny based edge detection
algorithm.
• For removal of noise from video signals, we
use Digital video broadcasting-cable (DVB-C).
It improves the Bit Error Rate (BER).
EDUCATIONAL PLAN
Educational activity:
Development of course:
• Design a complete course for digital image processing related to the
techniques used in this research based project such as,
– Theoretical and practical approach of identification of various types of noise.
– Theoretical and practical approach of noise variance estimation techniques.
– Theoretical and practical approach of constructing wavelets and multiwavelet
techniques.
– Theoretical and practical approach of developing univariate and multivariate
thresholding techniques.
– Theoretical and practical approach of compression and decompression
techniques.
•
•
•
•
•
Transmission techniques.
Development of Laboratory:
Personal Computers.
Matlab R2011b with complete range of Toolboxes.
Man power required to develop the new
innovative algorithms for proposed work:
• To hire faculty having good programming
skills, to develop the proposed algorithms in
Matlab.
• To involve M.Tech students by giving research
oriented projects in the proposed research
activity.
• To involve Ph.D scholars by formulating Ph.D
research problem in the proposed research
activity.
COLLABORATION PLAN
• Joginpally Bhaskar Institute of Engineering &
Technology, Lords Institute of Engineering &
Technology and Vidya Vikas Institute of Technology
are at a distance of 15 Kmts from each other and
one more mentee is at a distance of 300 kmts.
• In order to communicate and discuss on regular
basis, we have the facility to fix up a meeting, visits,
for the discussion on technical problem as the
partner institutions are nearby. we have good
internet facility, and teleconferencing .
• It helps in frequent discussions and exchange views
and the ideas on the proposed work.
OUT REACH PLAN
• In order to collect the required medical data,
we need to approach Hospitals.
• We need help from industry for the interfacing
of the proposed work.
ORGANIZATIONAL ASSISTANCE:
• We need help in the area of transmission of
images and video, from Information
Technology Research Academy (ITRA).
REFERENCES:
[1] Ce Liu, Richard Szeliski, Sing Bing Kang, C.Lawrence Zitinick, and William
T.Freeman, “Automatic estimation and removal of noise from a single image,”
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, no.2,
February 2008, pp.299-314.
[2] Damon M.Chandler and Sheila S.Hemami, “VSNR: A wavelet-based visual
signal-to-noise ratio for natural images,” IEEE Transactions on Image Processing,
vol 16, no.9, September 2007, pp.2284-2298.
[3] David L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on
Information Theory, vol.41, no.3, Mar1994, pp.613-627
[4] Downie.T.R., Silverman.B.W., “The discrete multiple wavelet transform and
thresholding methods,” IEEE Transactions on Signal Processing, vol. 46,
issue:9,1998, pp.2558-2561.
[5] F.Natterer, The mathematics of computerized tomography:Classics-in applied
mathematics, SIAM 32, July 2001, ISBN 0898714931
[6] Gabor T.Herman, “Fundamentals of computerized tomography:Image
reconstruction from projections, 2nd edition, Springer, 2010.
[7] Gilbert Strang and Troung Nguyen, “Wavelets and filter banks”, WillesleyCambridge press, Wellsley MA, First edition, 1996.
[8] Gilbert Strang, and Vasily Strela, “Short wavelets and matrix dilation equations,”
IEEE Transactions on Signal Processing, vol.43, no.1, January 1995, pp.108-115.
[9] Hancheng Yu, Li Zhao, and Haixian Wang, “Image denoising using Trivariate
shrinkage filter in the wavelet domain and joint bilateral filter in the spatial
domain,” IEEE Transaction on image processing, vol.18, no.10, October 2009,
pp.2364-2369.
[10] Hossein Rabbani, “Wavelet-domain medical image denoising using bivariate
Laplacian mixure model,” IEEE Transactions on Biomedical Engineering,
vol.56, no.12, December 2009, pp.2826-2837.
[11] Ivan W. Selesnic, Richard G. Baraniuk, and Nick G.Kingsbury, “The dual tree
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no.2, Mar.1992, pp. 587-607.
[12] John Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol.PAMI-8, no.6, November 1986, pp.679-698.
[13] Jo Yew Tham, Lixin Shen, Seng Luan Lee, and Hwee Huat Tan, “A general approach for analysis and
application of discrete multiwavelet transforms,” IEEE Transactions on Signal Processing, vol.48,
no.2, February 2000, pp.457-464.
[14] Kostadin Dabov, Alessandro Foi, Vladimir Karkovnik and Karen Egiazarian, “Image denoising by
sparse 3D transform domain collaborative filtering,” IEEE Transactions on Image Processing, vol.16,
no.8, August 2007.
[15] Landi G. and Loli Piccolomini.E., “An algorithm for image denoising with automatic noise
estimation,” Journal of Mathematical Imaging and Vision, vol.34, no.1, 2009, pp.98-106.
[16] M.Venu Gopala Rao & S.Vathsal, “Features preserved medical image denoising using steered
complex shrinkage algorithm,” International Journal of Electronics Engineering (IJEE), 1(1), 2009,
pp.19-26.
[17] M.Venu Gopala Rao and S.Vathsal, “Local adaptive bivariate shrinkage function for medical image
denoising,” International Journal of Electronics Engineering (IJEE), 1(1), 2009, pp.59-65.
[18] Michael B.Martin and Amy E.Bell, “New image compression techniques using multiwavelets and
multiwavelet packets,” IEEE Transactions on Image Processing, vol.10, no.4, April 2001, pp.500-510.
[19] Nam-Yong Lee Lucier, B.J “Wavelet methods for inverting the Radon transform with noisy data,” IEEE
Transactions on Image Processing, vol.10, issue.1, Jan. 2001, pp.79-94.
[20] Pierre Gravel, Gilles Beaudoin and Jacques A.De Guise, “A method for modeling noise in medical
images,” IEEE Transactions on Medical Imaging, vol.23, no.10, October 2004, pp.1221-1232.
[21] Pierrick Coupe, Jose V.Manjon, Elias Gedamu, Douglas Arnold, Montserrat Robles, D.Louis Collins,
“Robust Rician noise estimation for MR images,” Medical Image Analysis, vol.14, issue 4, August
2010, pp.483-493.
[22] Pizurica. A., Philips. W., Lemahieu I., and Acheroy. M., “A versatile wavelet domain noise filtration
technique for medical imaging,” IEEE Transactions on Image Processing , vol.22, no.3, Mar 2003,
pp.1062-1071.
[23] Prof.Syed Amjad Ali, Dr.Srinivasan Vathsal, and Dr.K.Lal Kishore,“An efficient denoising technique for
CT images using window-based multiwavelet transformation and thresholding,” European Journal of
Scientific Research, ISSN:1450-216X, vol. 48, no.2, 2010, pp.315-325
[24] Prof.Syed Amjad Ali, Dr.Srinivasan Vathsal, and Dr.K.Lal Kishore, “A GA- based window selection
methodology to enhance window-based multiwavelet transformation and thresholding aided CT
image denoising technique,” International Journal of Computer Science and Information Security,
vol.7, no.2, 2010, pp. 280-288.
[25] Prof.Syed Amjad Ali, Dr.Srinivasan Vathsal, Dr.K.Lal Kishore, “CT image denoising technique using GA
aided window-based multiwavelet transformation and thresholding with the incorporation of an
effective quality enhancement method,” International Journal of Digital Content Technology and its
Applications, volume 4, number 4, July 2010, pp.75-87.
[26] S.Grace Chang, “Adaptive wavelet thresholding for image denoising and compression,” IEEE
Transactions on Image Processing, vol. 9, no.9, September 2000, pp.1532-1546.
[27] Strela Vasily, Peter Niels Heller, Gilbert Strang, Pankaj Topiwala and Christopher Heil, “The
application of multiwavelet filterbanks to image processing,” IEEE Transactions on Image
Processing, vol no.8, issue no.4, Apr.1999, pp.548-563.
[28] Tai-Chiu Hsung, Daniel Pak-Kong Lun and K.C.Ho., “Optimizing the multiwavelet shrinkage
denoising,” IEEE Transactions on Signal Processing., vol.no.53, issue no.1, Jan 2005, pp.240-251.
[29] Jeny Rajan, Dirk Poot, Jaber Juntu and Jan Sijbers, “Noise measurement from magnitude MRI using
local estimates of variance and skewness,” Physics in Medicine and Biology, 55, (2010), pp.N441N449.
[30] Dr.P.Subashini, Bharathi.P.T, “Automatic noise identification in images using statistical features,”
International Journal of Computer Science and Technology, vol.2, issue 3, September 2011, pp.467471.
BUDGET:
Infrastructure
Personal Computers
Matlab Software(R2011b)
Related Toolboxes
: _________
: _________
: _________
: _________
Training expenses
: ________
Stationary
: ________
Miscellaneous expenses : ________
SUPPLEMENTARY MATERIAL:
Advisory board members:
Dr.Tanuja Srivastava
Professor, Department of Mathematics,
IIT, Roorkee- 247667, UP, INDIA.
01332-85084(O), 01332-85123(R), 01332-71793(R)
Dr.Umesh Kumar,
BARC, Trombay, Mumbai-400085.
umeshkum@barc.gov.in
Dr.C.Muralidhar,
Head, NDED, DRDL, Hyderabad-500258.
Dr.V.V.Rao,
Principal, JBIET, Yenkapally, R.R.Dist.
Dr.G.Durga Prasad,
Principal, Lords Institute of Engineering and Technology,
Himayathsagar, Hyderabad.
Collaborating organizations:
• Joginpally Bhaskar Institute of Engineering and
Technology.
• Lords Institute of Engineering and Technology.
• Vidya Vikas Institute of Technology.
• Narsaraopet Engineering College.