Possion Noise removal in MRI Data sets

8/10/2019 Possion Noise removal in MRI Data sets

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OPTIMAL INVERSION OF ANSCOMBE TRANSFORM AND FILTER

BASED POISSON NOISE REMOVAL IN MRI DATA SETS

1

Presented byG.Akshaya Karthika

II Year M.E.,(Communication Systems)

Under the Guidance ofMr.P.Karthikeyan.

Assistant ProfessorDepartment of ECE


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IMAGE DENOISING2

Noise reduction is the most important for enhancing color images.

The noise arises from improper lighting, movement of objects,sensitivity of imaging devices, resulting artifacts, blur & contrast

sensitivity.

The image acquisition sensor output carries both signal and noisecomponents which make high-quality image acquisition difficult.

Removal of Poisson noise is challenging because it is signaldependent. Its magnitude varies depend upon the intensity ofimage, especially it affects the medical images .


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Contd.3

Medical images(X-ray, CT,MRI) are mainly affected byPoisson noise .Because X-ray distribution follows thePoisson distribution.

Diagnosing of disease is complicated due to the noise. Theimportant information is removed if we perform the

denoising techniques.


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Points Observed From the LiteratureSurvey

4

1.V N Prudhvi and Dr T Venkateswarlu,Denoisingof MedicalImages using Total Variational MethodSignal & Image

Processing:An International Journal(SIPIJ)Vol.3,No.2 2012

Total Variational Method

Introduces Artifacts


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Contd..5

2.D.Mary Sugantharathnam and Dr.D.Manimegalai,TheCurvelet approach for Denoising invarious Imaging modalities Using different shrinkage rulesInternational Journal of Computer

Applications Volume 29-No.7,September 2011.

3.Wavelets, Ridgelets, and Curveletsfor Poisson Noise Removal

Bo Zhang, Jalal M. Fadili, and Jean-Luc StarckIEEE TRANSACTIONS ON IMAGEPROCESSING, VOL. 17, NO. 7, JULY 2008

Curvelet Approach

Fails to remove the Poissonnoise in medical images.


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Contd..6

4.Mirela Frands,IsabelleE.Magnin (2011) proposed Wavelet thresholding- Based denoisingMethod of list mode NLM algorithm for compton imaging,International Journal of ComputerApplications

5.Wavelet-Domain Medical Image Denoising UsingBivariate Laplacian Mixture Model

Hossein Rabbani, Member, IEEE, Reza Nezafat, and Saeed Gazor

, Senior Member, IEEEIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 56, NO. 12, DECEMBER 2009

Multiwavelet domain

Only suitable for the x-rayimages.


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Contd..7

6.J.Salmon, C-A.Deledalley, R.Willett, Z.Harmany,PoissonNoise Reduction with Non-local PCA,Duke Univeresity,ECE

Department Durham,NC,USA,CEREMADE,CNRS-paris-Dauphine,Paris,France

PCA (Principle Component Analysis)

Dimension reduction is needed


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Contd..8

7. J. Bednar and T.L. Watt (1984), Alpha-trimmed means

and their relationship to median filters, IEEE TransAcoust.,Speech, Signal Processing, Vol. 32, No.1, pp.

145-153..

Median Filter

Suitable for Salt & PepperNoise


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Contd..9

8.Hakan Gray Senel, Richard Alan Peters and BenoitDawant. 2002. Topological Median Filter. IEEE Trans onImage Processing. 11(2):89 -104.

Linear filter

Poor performance on Gaussiannoise


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Contd..10

9.Gaussian Noise Filtering from ECG by Wiener Filter and EnsembleEmpirical Mode Decomposition

Kang-Ming Chang & Shing-Hong LiuReceived: 24 May 2009 / Revised: 13October 2009 / Accepted: 28 December2009

# 2010 Springer Science+Business Media, LLC. Manufactured in TheUnited States

Wiener Filtering(UniformFiltering throughout The Image)

Blurring of Fine Detail


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Contd..11

10.Optimal Inversion of the Anscombe Transformationin Low-CountPoisson Image DenoisingMarkku Mkitalo and Alessandro Foi IEEETransactions on image processing, vol. 20, no. 1, January 2011

Anscombe transform

Used to convert Poissonnoise into Gaussian


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PROBLEM IDENTIFICATION12

MRI images are affected by 70% poisson noise, 20% Gaussian noiseand 10% other noises.

So phase II a method for removing both Poisson noise andGaussian noise is to be proposed.


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OBJECTIVE13

To Implement an efficient algorithm for removing bothNoises.

Analyzing various filters for removing Gaussian noise .

and choose the best filter for Gaussian noise removal.


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FLOW OF WORK14

1. Anscombetransform

2. Gaussian removal

Filter

3.Optimal InversionTransform

Desired Denoised Output

Poisson Noise AddedImage

Converted to GaussianNoise

Gaussian DenoisedOutput


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POISSON NOISE15

Poisson noise is defined by

variance is not uniform .

!iyZiii ZeyyZP ii


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ANSCOMBE TRANSFORM16

Step 1:

Stabilize the variance into unitary variance .

Convert the Poisson noise into Gaussian noise.

Anscombe transform is given by

8

32 ZZf


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17

INPUT IMAGE NOISY IMAGE


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Output of Anscombe Transform18


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Gaussian noise Removal Filter

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Step 2:

Comparing various filters used for Gaussian noiseremoval.

o Gaussian

o Wiener filter

o Bilateral

o BM3D

Choose a suitable filter for Anscombe Transform


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GAUSSIAN FILTER20

Gaussian filter smooths the image by calculating weighted averagein filter box

The weight factor W(x , y)=

Where a is the standard deviation a=

DISADVANTAGE

It does not preserve the edges

a

e

2

22

.2ryx


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Wiener filter21

The Wiener deconvolution method has widespread use in imagedeconvolution applications, as the frequency spectrum of most visual

images is fairly well behaved and may be estimated easily.

The frequency Response is given by

DISADVANTAGE:

Introduces bluring effect

fNfSfH

fSfHfG

2

*


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BM3D filter22

1) finding the image patches similar to a given image patch and grouping them in a3Dblock

2) 3D linear transform of the 3D block;

3) shrinkage of the transform spectrum coeffcients;

4)inverse 3D transformation

DISADVANTAGE:

For higher noise variance it shows poor performance.


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Bilateral filter23

The bilateral filter is also defined as a weighted averageof nearby pixels,in a manner very similar to Gaussianconvolution.

The difference is that the bilateral filter takes into accountthe difference in value with the neighbours to preserveedges while smoothing.


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The key idea of the bilateral filter is that for a pixel toinfluence another pixel, it should not only occupy anearby location but also have a similar value.

where are spatial parameter and range parameter

qqprsq sp IIIGqpGwIBF 1

][

rs ,


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Bilateral filter Advantages25

Its formulation is simple: each pixel is replaced by a weightedaverage of its neighbors. This aspect is important because it makes iteasy to acquire intuition about its behavior, to adapt it to application-specific requirements, and to implement it.

It depends only on two parameters that indicate the size and

contrast of the features to preserve.

It can be used in a non-iterative manner. This makes theparameters easy to set since their effect is not cumulative

over several iterations.


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Filter output26

GAUSSIAN FILTEROUTPUT

WIENER FILTEROUTPUT


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Schedule27

BM3D FILTER

OUTPUTBILATERAL FILTER

OUTPUT


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Inverse Anscombe Transform28

Step:3

Inverse transform is given by

!

.8

32}/{

0 Z

eyzyzfE

yz

z

R t ti i I A b


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Reconstruction using Inverse AnscombeTransformation

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Gaussian Filter Wiener FIlter


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BILATERAL FILTERBM3D FILTER


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Performance Analysis31

.Input Images

GAT+Gaussian GAT+ Wiener GAT+ BM3D GAT+Bilateral

MSE PSNR MSE PSNR MSE PSNR MSE PSNR

0.34 52.81 0.17 55.78 0.02 66.25 0.01 66.47

0.25 54.12 0.06 60.13 0.01 67.29 0.01 68.03

0.23 54.57 0.05 61.46 0.01 67.61 0.01 68.48

0.51 51.02 0.14 56.68 0.02 65.03 0.02 65.86

0.34 52.08 0.10 58.01 0.01 67.43 0.01 67.47


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References32

1.Isabel Rodrigues and Joao Sanches,Denoisingof Medical ImagesCorrupted by Poisson Noise, IEEE Communication society 2008.

2.D.Mary Sugantharathnam and Dr.D.Manimegalai,TheCurvelet

approach for Denoising in various Imaging modalities Usingdifferent shrinkage rulesInternational Journal of ComputerApplications Volume 29-No.7,September 2011.

3.V N Prudhvi and Dr TVenkateswarlu,Denoisingof Medical Imagesusing Total Variational MethodSignal & Image Processing:An

International Journal(SIPIJ)Vol.3,No.2 2012


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Contd33

4.Mary sugantharathnam, manimegalai(2011) proposed the curveletapproach for denoising in various imaging modalities.

5.V N Prudhvi Raj,Dr T Venkateswarlu(2012) proposed TotalVariational method for denosing the Medical images.

6.Mirela Frands,IsabelleE.Magnin (2011) proposed Waveletthresholding- Based denoising Method of list mode MLEMalgorithm for compton imaging

7.Florian Luisier, Member,IEEE,Thierry Blu,senior

member,IEEE,and Micheel Unser,Fellow,IEEEImagedenoising in mixed poisson-gaussian noiseIEEEtransactionson image processing,Vol.20,No.3,March 2011


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Contd34

8. J. Bednar and T.L. Watt (1984), Alpha-trimmed meansand their relationship tomedian filters, IEEE TransAcoust., Speech, Signal Processing, Vol. 32, No.1,

pp.145-153..

9.Hakan Gray Senel, Richard Alan Peters and Benoit Dawant. 2002.Topological Median Filter. IEEE Trans on Image Processing. 11(2):89 -

10410.Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble

Empirical Mode Decomposition

Kang-Ming Chang & Shing-Hong LiuReceived: 24 May 2009 / Revised:13 October 2009 / Accepted: 28 December2009 # 2010 Springer

Science+Business Media, LLC. Manufactured in The United States


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Possion Noise removal in MRI Data sets