PRINCIPAL INVESTIGATOR: (Mentor) - PowerPoint PPT Presentation

1 / 24
About This Presentation
Title:

PRINCIPAL INVESTIGATOR: (Mentor)

Description:

Title: PowerPoint Presentation Author: SYED AMJAD ALI Last modified by: SYED AMJAD ALI Created Date: 8/16/2006 12:00:00 AM Document presentation format – PowerPoint PPT presentation

Number of Views:68
Avg rating:3.0/5.0
Slides: 25
Provided by: SYE80
Category:

less

Transcript and Presenter's Notes

Title: PRINCIPAL INVESTIGATOR: (Mentor)


1
  • PRINCIPAL INVESTIGATOR (Mentor)
  • Dr.Srinivasan Vathsal
  • Professor and Head, Dept of EEE, Dean RD,
  • JBIET, Yenkapally, Moinabad, RR Dist.
  • svathsal_at_gmail.com
  • 07702299775
  • CO-PRINCIPAL INVESTIGATORS (Mentee)
  • Dr.Syed Amjad Ali
  • Professor, Department of ECE
  • Sy.No32, Lords Institute of Engineering and
    Technology,
  • Himayath sagar, Hyderabad.
  • Syedamjadali.lords_at_gmail.com

2
(No Transcript)
3
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.

4
  • 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.

5
  • 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.

6
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.

7
  • 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
    wavelets7, 9-11, 16,17, 19, 20, 22,
    26 and multiwavelet techniques4, 8, 13,
    27, 28 for decomposition.
  • Developed univariate and multivariate
    thresholding techniques3 for denoising.
  • Developed a modified visual quality enhancement
    techniques of images
  • i) using modified Canny based edge detection
  • algorithms12.
  • ii)using modified morphological thinning
    operation.
  • Developed lossy and lossless compression
    techniques18 for compression of an image. We
    have applied the above techniques for the still
    and natural MRI images.

8
  • To still images, we have added additive white
    Gaussian noise at different noise levels and
    denoised using window based multiwavelet
    transformation14, 23 and thresholding
    techniques.
  • We developed genetic algorithm based window
    selection24 for the optimization of windowing
    technique.
  • We have also developed estimation techniques1,
    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.

9
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.

10
  • 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.

11
  • 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.

12
  • 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).

13
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.

14
  • 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.

15
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.

16
  • 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).

17
  • 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,
  • issue9,1998, pp.2558-2561.
  •  
  • 5 F.Natterer, The mathematics of computerized
    tomographyClassics-in applied

18
  • 6 Gabor T.Herman, Fundamentals of computerized
    tomographyImage
  • reconstruction from projections, 2nd
    edition, Springer, 2010.
  •  
  • 7 Gilbert Strang and Troung Nguyen, Wavelets
    and filter banks, Willesley-
  • Cambridge 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.

19
  • 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.

20
  • 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

21
  •  
  • 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.N441-

22
  • BUDGET
  • Infrastructure _________
  • Personal Computers _________
  • Matlab Software(R2011b) _________
  • Related Toolboxes _________
  • Training expenses ________
  • Stationary
    ________
  • Miscellaneous expenses ________
  •  

23
  • 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_at_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,

24
  • Collaborating organizations
  • Joginpally Bhaskar Institute of Engineering and
    Technology.
  • Lords Institute of Engineering and Technology.
  • Vidya Vikas Institute of Technology.
  • Narsaraopet Engineering College.
Write a Comment
User Comments (0)
About PowerShow.com