Ultrasound

UltrasoundIntroductionSRAD FilterWiener FilterSNR comparisonDiagnosis using ANN1IntroductionHistory :Sound technology was utilized in the early nineteenth century for measuring distances underwater. In 1958, Donald and his colleagues published the most important paper in biomedical ultrasound.Definition : Waves of higher frequency than audible sound waves. It is approximately 20 KHz.

2Ultrasonography : It is an ultrasound-based diagnostic imaging technique used to visualize body structures including muscles, joints, vessels and internal organs. The frequencies of sound waves used in ultrasonography are between about (1 and 15) MHz.

Introduction

3IntroductionHow are images acquired for ultrasound ?

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AdvantagesDisadvantagesImage muscle, soft tissue, and bone surfaces very well.Show the structure of organs.No known long-term side effects.Equipment is widely available & flexible.Relatively inexpensive compared to other modes of investigation.

Sonographic devices have trouble penetrating bone.The depth of penetration of ultrasound may be limited depending on the frequency of imaging.Operator-dependent.

Introduction5Ultrasound Vs X-Ray :IntroductionUltrasoundX-RayPenetratingLowHighSafetyVery safe (sound waves)Hazardous(ionizing radiation)ResolutionRelatively poorHigherShow Can show moving thingsShow only one moment at a timePregnancyRecommendedCant be used Frequency Range20KHz 200MHz30 1015 30 1018 Hz

Cost$100 - $1,000 Less than $1006Despeckle FilteringImages acquired by ultrasound are corrupted by speckles. Speckle noise : Here the speckle phenomenon results from the constructive-destructive interference of the coherent ultrasound pulses back scattered from the tissues.We have 2 types of despeckle filters :SRAD FilterWiener Filter

7SRAD FilterSRAD : Speckle Reducing Anisotropic Diffusion.SRAD : non-linear technique for multiplicative noise reduction and image enhancement.

SRAD concept : 1. making intra-region smoothing (in homogeneous regions) and preserve edges. q (coefficient of variation) controls this variation. 2. using PDE approach for speckle removal.

8SRAD Filterq (coefficient of variation) : achieves a balance between : 1. smoothing in homogeneous regions (average-like filtering). 2. preserving edges (identity filtering).

How is this balance achieved ? using thresholds to alter the performance between average-like filtering (in homogeneous regions) & identity filtering (at edges). 9SRAD FilterWhy SRAD ? 1. SRAD is not sensitive to the size of filter window (as in conventional despeckle filters). 2. SRAD doesnt utilize hard thresholds , as hard thresholds lead to blotching the images. 3. SRAD not only preserves edges but also enhance edges by inhibiting the diffusion process near edges.

10SRAD FilterSRAD Performance : speckle removal is achived using SRAD PDE approach : Where : c(q) : diffusion coefficient. Etymology : c(q) is anisotropic (direction variant properties) so , this method of removing speckle noise is called Speckle Reducing Anisotropic Diffusion or SRAD.

11SRAD FilterSolution of SRAD PDE : (discretization) t = n t , n = 0 , 1 , 2 , . , t is iteration step size. x = ih , i = 0 , 1 , 2 , .. , M-1. y = jh , j = 0 , 1 , 2 , , N-1.

By this way of discretization , then Mh Nh is the size of the image. Let according to the previous assumptions will be : = I (ih , jh , n t ).

12SRAD FilterFirst Stage : calculate the .Second Stage : calculate diffusion coefficient c(q).Third Stage : calculate the divergence of which is

Finally : we can substitue using the previous 3 stages to reach the final result which is :

= +

This iterative relation shows that we need much computation time , and also shows how to control the diffusion process.

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SRAD FilterFlow Chart :14startClose figures &Clear variablesReading the imageChoose a rect. of the desired part of imageApplying SRAD filter on imageWait for processing and loading according to the no. of iterationsGetting O/P filtered imageEndSNR CalculationWe can use the following equation to get the SNR value :

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SNR CalculationFlow Chart :

no

yes16startGet no. of rows & columns MNSet initial value for numeratorLoop for numeratorLoop finished ?Wait for summation of elementsSNR Calculation no

yes17Set initial value for denominatorLoop for denominatorLoop finished ?Wait for summation of elementsSNR =numerator / denominator EndResultsUltrasound on fetus : (SNR = 23.658)

18ResultsSpina Pifida : (SNR = 17.868)

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ResultsHydrocephalus : (SNR = 35.424)

20ResultsKidney dysplasia : (SNR =67.094)

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ResultsAtrial septal defect : (SNR =53.862) 22

ResultsFatty liver : (SNR =53.767) 23

ResultsDiaphragmatic hernia : (SNR =26.468) 24

ResultsCerebral Palsy : (SNR =25.579) 25

Conclusion SRAD filter is a good way that removes speckle noise from ultrasound images efficiently and makes intra-region smoothing (in homogeneous regions) and preserve edges.

SRAD takes long computation time for iterations & has relatively lower SNR value.

26Wiener FilterProposed by Norbert Wiener in 1940s.Purpose : filter out speckle noise that has corrupted the image, based on statistical approach.Method : Its purpose is to reduce the amount of noise present in the image by comparison with an estimation of the desired noiseless image.It is characterized by : The optimal estimator (in the sense of Mean Squared Error (MSE)) for stationary Gaussian process.

27Equation :

Duo to :

so, we cant use Wiener filter for true images. We can use Local Adaptive Wiener Filter

Wiener Filter28

Local Adaptive Wiener FilterWhy Local : Because image isnt stationary as the assuumption of Wiener filter. But, we can say Locally stationary (samples of image are stationary and samples are nonstatinary). That is done using {LLMMSE} Why Adaptive : Because it is used to determine the nonstationary weights that are in the image due to its abrupt changes of image intensities. That is done using {AWA} function.

In general we can say that ,it can smooth image and leave edges with out change.29 Consider the filtering of images corrupted by image independent zero-mean white Gaussian noise. The problem can be modeled as :

Where :y(i, j) : the noisy measurement.x(i, j) : the noise free image.n(i,j) : additive Gaussian noise.

Where : N : the number of elements in x(i,j).

Local Adaptive Wiener Filter30When x(i, j) and n(i, j) are stationary Gaussian processes

Where :x : the variance of image . x : the mean of image.n : the mean of noise and assumed to be zero.

Here we assume that we know the mean and the variance of the image from the characteristics of stationary Gaussian process .

Local Adaptive Wiener Filter31 By tacking uniform moving average window of size (2r + 1) (2r + 1)We can get Local Mean and Local Variance as shown:

Till now , the resulting denoised image is poor and looks noisy.To blur the mean and increase the variance near edges,We use weighted form

Local Adaptive Wiener Filter32Where :

Where The parameters of this weighting function are:> 0 = 2.5 n 1

Local Adaptive Wiener Filter33Flow Chart :

34Local Adaptive Wiener FilterstartClose figures &Clear variablesReading the imageApplying Wiener filter on imageGetting O/P filtered imageEndConvert Uint8 to DoubleCalculate SNR

Ultrasound on fetus : (SNR = 28.371)

Results35ResultsSpina Bifida : (SNR = 28.566)

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Hydrocephalus : (SNR = 45.566)

Results37ResultsKidney dysplasia : (SNR = 78.592)

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ResultsAtrial Septal Defect : (SNR =65.162)39

ResultsFatty Liver : (SNR =62.416) 40

ResultsDiaphragmatic hernia : (SNR =30.373)41

ResultsCerebral Palsy : (SNR =43.080)42

SRAD Vs WienerIn Wiener , we reduce the amount of noise present in the image by comparison with an estimation of the desired noiseless image. But no comparison in SRAD.In SRAD , we need much computation time as a result of many iteration loops. But in Wiener we dont.Wiener is linear and SRAD is non-linear.SNR of image filtered by SRAD is a bit less than Wiener. 43SNR Comparison SNR (Wiener)SNR (SRAD)Ultrasound on Fetus28.37123.658Spina Pifida28.56617.868Hydrocephalus45.31835.424Kidney Dysplasia78.59267.094Atrial Septal Defect65.16253.862Fatty Liver62.41653.767Diaphragmatic hernia30.37326.468Cerebral Palsy43.08025.57944 The Local Adaptive Wiener Filter is a good mean to reduce the amount of speckles noise in the images.

It has a higher SNR than SRAD which means a higher quality of image.

No long computation time.

Conclusion45ANN is divided into TWO stages:The first one is TRAINING.The second one RECALLING.Training: Is to learn the network the image and its number with its disease name.Recalling: Is to give the number of the image to the network and it will give you the image with its disease name.

46Diagnosis using ANNTraining:

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Recalling:If you Press 1 :

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