Ultrasound Introduction SRAD Filter Wiener Filter SNR comparison Diagnosis using ANN

Introduction History : 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.

Introduction Ultrasonography : 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 How are images acquired for ultrasound ?

Introduction Advantages Disadvantages Image 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.

Show only one moment at a time Introduction Ultrasound Vs X-Ray : Ultrasound X-Ray Penetrating Low High Safety Very safe (sound waves) Hazardous (ionizing radiation) Resolution Relatively poor Higher Show Can show moving things Show only one moment at a time Pregnancy Recommended Can’t be used Frequency Range 20KHz – 200MHz 30 × 1015 – 30 × 1018 Hz Cost $100 - $1,000 Less than $100

Despeckle Filtering Images 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 Filter Wiener Filter

SRAD Filter SRAD : 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.

SRAD Filter “q” (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).

SRAD Filter Why SRAD ? 1. SRAD is not sensitive to the size of filter window (as in conventional despeckle filters). 2. SRAD doesn’t 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.

SRAD Filter SRAD 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.

SRAD Filter Solution 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 х Nh is the size of the image. Let according to the previous assumptions will be : = I (ih , jh , n ∆t ).

SRAD Filter First 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.

SRAD Filter Flow Chart : Applying SRAD filter on image Close figures & start Applying SRAD filter on image Close figures & Clear variables Reading the image Wait for processing and loading according to the no. of iterations Choose a rect. of the desired part of image Getting O/P filtered image End

SNR Calculation We can use the following equation to get the SNR value :

SNR Calculation Flow Chart : no yes start Get no. of rows & columns M×N Set initial value for numerator Loop for numerator Loop finished ? Wait for summation of elements

SNR Calculation no yes Set initial value for denominator Loop for denominator Loop finished ? Wait for summation of elements SNR =numerator / denominator End

Results Ultrasound on fetus : (SNR = 23.658)

Results Spina Pifida : (SNR = 17.868)

Results Hydrocephalus : (SNR = 35.424)

Results Kidney dysplasia : (SNR =67.094)

Results Atrial septal defect : (SNR =53.862)

Results Fatty liver : (SNR =53.767)

Results Diaphragmatic hernia : (SNR =26.468)

Results Cerebral Palsy : (SNR =25.579)

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.

Wiener Filter Proposed 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.

Wiener Filter Equation : Duo to : so, we can’t use Wiener filter for true images. We can use Local Adaptive Wiener Filter

Local Adaptive Wiener Filter Why Local : Because image isn’t 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.

Local Adaptive Wiener Filter 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 Filter When x(i, j) and n(i, j) are stationary Gaussian processes Where : σ²x : the variance of image . μx : the mean of image. μn : 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 Filter 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 Filter Where : Where The parameters of this weighting function are: α> 0 ε = 2.5 σn α ε² ≥ 1

Local Adaptive Wiener Filter Flow Chart : start Applying Wiener filter on image Close figures & Clear variables Reading the image Getting O/P filtered image Calculate SNR Convert Uint8 to Double End

Results Ultrasound on fetus : (SNR = 28.371)

Results Spina Bifida : (SNR = 28.566)

Results Hydrocephalus : (SNR = 45.566)

Results Kidney dysplasia : (SNR = 78.592)

Results Atrial Septal Defect : (SNR =65.162)

Results Fatty Liver : (SNR =62.416)

Results Diaphragmatic hernia : (SNR =30.373)

Results Cerebral Palsy : (SNR =43.080)

SRAD Vs Wiener In 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 don’t. Wiener is linear and SRAD is non-linear. SNR of image filtered by SRAD is a bit less than Wiener.

SNR Comparison SNR (Wiener) SNR (SRAD) Ultrasound on Fetus 28.371 23.658 Spina Pifida 28.566 17.868 Hydrocephalus 45.318 35.424 Kidney Dysplasia 78.592 67.094 Atrial Septal Defect 65.162 53.862 Fatty Liver 62.416 53.767 Diaphragmatic hernia 30.373 26.468 Cerebral Palsy 43.080 25.579

Conclusion 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.

Diagnosis using ANN ANN 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.

Results Training:

Results

Results

Results Recalling: If you Press 1 :

Results If you Press 3 :

Result If you Press 4 :

Result If you Press 8 :