Enrichment of Ultrasound Images using Contrast ...

International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438

Enrichment of Ultrasound Images using Contrast Enhancement Techniques Yousif Mohamed Y. Abdallah1, 2, Amel S. Algaddal3, Mommen A. Alkhir4 1

College of Applied Medical Science, Almajmah University, Riyadh, Saudi Arabia

2

Sudan University of Science and Technology, College of Medical Radiological Science, Khartoum, Sudan 3

Sebha Infertility Center, Sebha, Libya

4

College of Applied Medical Science, Sebha University, Libya

Abstract: Ultrasound is one of the diagnostic equipments to diagnose human internal organs, tendons, to capture their size and structure. Ultrasound is usually used for pregnancy, as the images for others' internal organs that are not very clear. The main objective of this paper was to study the enhancement of ultrasound image using filtering technique. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were Top-hat filtering and Deblurring Images Using the Blind Deconvolution Algorithm. In this paper, prominent constraints are firstly preservation of image's overall look; secondly preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image.

Keywords: Ultrasound, Matlab, Contrast Enhancement Techniques

1. Introduction Ultrasound or ultrasonography is a medical imaging technique that uses high frequency sound waves and their echoes. The technique is similar to the echolocation used by bats, whales and dolphins, as well as SONAR used by submarines. Ultrasound allows one to visualize and therefore examine a part of the human anatomy in medicine. The range of mechanisms associated with the interaction of ultrasound with biological and other media results in extremely rich encoding of information on echo signals. A substantial amount of the progress in medical ultrasound over the last 30 or so years has been due to the still on-going process of learning how to design the signal and image processing components of scanners so as to best cope with and take advantage of this richness. For example, it is frequently necessary to separate information of different kinds, compensate for interaction between variables, deal with a very wide dynamic range, cope with rather specific kinds of noise, remove artifacts, and display the information in a manner best suited for human observation. Such functions are achieved through appropriate processing that takes place at one or more of a number of the levels [1-4]. Median filter now is broadly used in reducing noise and smoothing the images. The filter preserves monotonic image features that fill more than half the area of the transform window. Based on Median filter, many scientists have used Topological Median filter to improve conventional Median filter. The Topological Median filters implements some existing ideas and some new ideas on fuzzy connectedness to improve, over a conventional Median filter, the extraction of edges in noise. The Topological Median filters defined are outperforming conventional Median filters with 7 x 7 or larger transform windows in reducing a noise while preserving edges. On the average, there is a minimal effect on edge strength or edge location. A conventional Median filter does outperform a

Paper ID: SUB15718

Topological Median filter in the reduction of the amplitude of noise. Through experiments the variance of noise passed through a Topological Median filter was found to exceed that of a conventional Median filter by a factor of about 1.25. The better performance of the Topological Median filters over conventional Median filters is in maintaining edge sharpness, edge magnitude and edge location. Conventional Median filters reduce the variance of noise more than Topological Median filters [5-8]. An adaptive two-pass Median filter to remove impulsive noise. An image contaminated by impulsive noise is represented in a two-pass Median filtering and is processed by a Median filter twice. By analyzing the spatial distribution, i.e., the error index matrix of the impulsive noise, the adaptive two-pass Median filter looks for columns containing over-corrected pixels by the standard Median filter and replaces over-corrected pixels by their original values. The experiment that has been done shows that the adaptive filter is able to reduce the mean square (MSE) and mean absolute error (MAE) produced better results. An algorithm for image denoising based on Average filter with maximization and minimization for the smoothness of the region, unidirectional Median filter for edge region and Median filter for the indefinite region. It was discovered that when the image is corrupted by both Gaussian and impulse noises, neither Average filter nor Median filter algorithm will obtain a result good enough to filter the noises because of their algorithm. The image is divided into different regions using neighborhood contrast intensity and employ different methods to denoise the pixels in different regions. This is not only to maintain the characteristic that the Average filter algorithm has a better denoising effect on Gaussian noise, but take into account that Median filter algorithm can better preserve the details. Experiments that been done show that the proposed algorithm is practicable and competent [9-10]. Wiener filters are extensively used for inverse problems. Wiener filter provides the best restored signal with respect to the square error averaged over the

Volume 4 Issue 1, January 2015 www.ijsr.net Licensed Under Creative Commons Attribution CC BY

2381

International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438 original signal and the noise among linear operators. Based on Wiener filter method, F. Jin et al. considered the adaptive Wiener filtering of noisy images and image sequences. An adaptive weighted averaging approach was used to estimate the second-order statistics required by the Wiener filter. From the experiment, it shows that the result from Wiener filter has improved the peak-to-peak signal noise ratio (PSNR) by about 1dB. It has also improved the annoying boundary noise significantly [11-13].

get rid the noise, blurring and unwanted information. The main objective of this study was to study the enhancement of ultrasound image using filtering technique. It performed morphological top-hat filtering on the grayscale or binary input image IM figure 1 and figure 2.

2. Methods and Materials This was experimental work carried out to enhance the quality of ultrasound images through removal of speckle noise using techniques such as Median, Average and Wiener Filtering. The median filter is a non-linear digital filtering technique; it used to remove noise from images. It is useful to reduce speckle noise and salt and pepper noise. Its edgepreserving nature makes it practical in cases where edge blurring is undesirable. Average Filter performs spatial filtering on each individual pixel in an image using the grey level values in a square or rectangular window surrounding each pixel. Image analysis using MatLab: Top-hat filtering computes the morphological opening of the image (using imopen) and then subtracts the result from the original image. Imtophat uses the structuring element SE, where SE is returned by strel. SE must be a single structuring element object, not an array containing multiple structuring element objects. The Blind Deconvolution Algorithm can be used effectively when no information about the distortion (blurring and noise) is known. The algorithm restores the image and the point-spread function (PSF) simultaneously. The accelerated, damped Richardson-Lucy algorithm is used in each iteration. Additional optical system (e.g. camera) characteristics can be used as input parameters that could help to improve the quality of the image restoration. PSF constraints can be passed in through a user-specified function. The steps of image analysis Step 1: Image Reading Step 2: A Blur Simulation Step 3: The Blurred Image Using PSFs of Various Sizes Restoration Step 4: The Restored PSF Analysis Step 5: The Restoration Improvement Step 6: Using Additional Constraints on the PSF Restoration

Figure 1: Original Ultrasound image

Figure 2: Enhanced Ultrasound using Top-hat filtering 1. Deblurring Images of ultrasound The Blind Deconvolution Algorithm can be used effectively when no information about the distortion (blurring and noise) is known. The algorithm restores the image and the pointspread function (PSF) simultaneously. The accelerated, damped Richardson-Lucy algorithm is used in each iteration. Additional optical system (e.g. camera) characteristics can be used as input parameters that could help to improve the quality of the image restoration. PSF constraints can be passed in through a user-specified function. Step 1: Image Reading The example reads in an intensity image. The deconvblind function can handle arrays of any dimension figure 3.

3. The Results Ultrasound images in general are complex due to data composition, which can be described in terms of speckle information. Upon visual inspection, speckle noise consists of a relatively high grey level intensity, qualitatively ranging between hyperechoic (bright) and hypoechoic (dark) domains. In addition, ultrasound images have the advantage of being non-invasive, portable, versatile, and low cost and not requiring ionizing radiations. Therefore, those images need to be modified by using image processing program to

Paper ID: SUB15718

Figure 3: Original Ultrasound image


2382

International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438 Step 2: A Blur Simulation Simulate a real-life image that could be blurred (e.g., due to camera motion or lack of focus). The example simulates the blur by convolving a Gaussian filter with the true image (using imfilter). The Gaussian filter then represents a pointspread function, PSF figure 4.

The third restoration, J3 and P3, uses an array of ones, INITPSF, for an initial PSF that is exactly of the same size as the true PSF figure 7.

Figure 7: Shows Deblurring with INITPSF for U/S image Figure 4: A blurred U/S image Step 3: The Blurred Image Using PSFs of Various Sizes Restoration To illustrate the importance of knowing the size of the true PSF, this example performs three restorations. Each time the PSF reconstruction starts from a uniform array--an array of ones. The first restoration, J1 and P1, uses an undersized array, UNDERPSF, for an initial guess of the PSF. The size of the UNDERPSF array is 4 pixels shorter in each dimension than the true PSF figure 5.

Figure 5: A blurred U/S image using Undersized PSF The second restoration, J2 and P2, uses an array of ones, OVERPSF, for an initial PSF that is 4 pixels longer in each dimension than the true PSF figure 6.

Figure 6: Deblurring with Oversized PSF for U/S image

Paper ID: SUB15718

Step 4: The Restored PSF Analysis All three restorations also produce a PSF. The following pictures show how the analysis of the reconstructed PSF might help in guessing the right size for the initial PSF. In the true PSF, a Gaussian filter, the maximum values are at the center (white) and diminish at the borders (black) figure 8.

Figure 8: The restored PSF for U/S image Analyzing The PSF reconstructed in the first restoration, P1, obviously does not fit into the constrained size. It has a strong signal variation at the borders. The corresponding image, J1, does not show any improved clarity vs. the blurred image, Blurred. The PSF reconstructed in the second restoration, P2, becomes very smooth at the edges. This implies that the restoration can handle a PSF of a smaller size. The corresponding image, J2, shows some deblurring but it is strongly corrupted by the ringing. Finally, the PSF reconstructed in the third restoration, P3, is somewhat intermediate between P1 and P2. The array, P3, resembles the true PSF very well. The corresponding image, J3, shows significant improvement; however it is still corrupted by the ringing. Step 5: Improving the Restoration The ringing in the restored image, J3, occurs along the areas


2383

International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438 of sharp intensity contrast in the image and along the image borders. This example shows how to reduce the ringing effect by specifying a weighting function. The algorithm weights each pixel according to the WEIGHT array while restoring the image and the PSF. In our example, we start by finding the "sharp" pixels using the edge function. By trial and error, we determine that a desirable threshold level is 0.3 figure 9. To widen the area, we use imdilate and pass in a structuring element, se.

Figure 11: Anonymous function of Deblurred Image If the oversized initial PSF, OVERPSF, had used without the constraining function, FUN, the resulting image would be similar to the unsatisfactory result, J2, achieved in Step 3 as shown in figure 11.

4. Conclusion Figure 9: The Restoration of U/S image improvement Step 6: Using Additional Constraints on the PSF Restoration The function, FUN, below returns a modified PSF array which deconvblind uses for the next iteration. In this example, FUN modifies the PSF by cropping it by P1 and P2 number of pixels in each dimension, and then padding the array back to its original size with zeros. This operation does not change the values in the center of the PSF, but effectively reduces the PSF size by 2*P1 and 2*P2 pixels as shown in figure 10.

Figure 10: The deblurred Image The anonymous function, FUN, is passed into deconvblind last. In this example, the size of the initial PSF, OVERPSF, is 4 pixels larger than the true PSF. Setting P1=2 and P2=2 as parameters in FUN effectively makes the valuable space in OVERPSF the same size as the true PSF. Therefore, the outcome, JF and PF, is similar to the result of deconvolution with the right sized PSF and no FUN call, J and P, from step 4 as shown in figure 11.

Paper ID: SUB15718

Ultrasound images in general are complex due to data composition, which can be described in terms of speckle information. Upon visual inspection, speckle noise consists of a relatively high grey level intensity, qualitatively ranging between hyperechoic (bright) and hypoechoic (dark) domains. In addition, ultrasound images have the advantage of being non-invasive, portable, versatile, and low cost and not requiring ionizing radiations. Therefore, those images need to be modified by using image processing program to get rid the noise, blurring and unwanted information .This was an experimental study to study the enhancement of ultrasound image using filtering technique using image processing technique (MatLab version R2009a). In addition to evaluate contrast enhancement pattern in different ultrasound images such as grey color in order to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in panoramic images. The main techniques of enhancement used in this study was Top-hat filtering which computed the morphological opening of the image (using imopen) and then subtracts the result from the original image. The anther technique was Deblurring Images Using the Blind Deconvolution Algorithm which can be used effectively when no information about the distortion (blurring and noise) is known. The results of this study were agreed with previous studies in blind Deconvolution algorithm and it added new approach by using both technique in U/S image processing which would increase the diagnostic value of those images. So conclusion of this research that the new approach is funded on an attempt to interpret the problem from the view of blind source separation (BSS), thus to see the U/S image as a simple mixture of (unwanted) background information, diagnostic information and noise and filtered it. The detection of the noise is a complex procedure which is difficult to detect by naked eye so that image analysis should be performed by using powerful image processing.


2384

International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438 Sudan University of Science and Technology. He is now assistant professor and Consultant Radiation Therapist.

References [1] Brox T., Weickert J., Burgeth B., Mrazek P, 2006, Nonlinear Structure Tensors, Image and Vision Computing, Vol 24(1), pp 41-55. [2] Chambolle. A, 2004,An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, Vol 20,pp 89–97. [3] Chan T., Osher S., Shen J.,2001, The digital TV filter and nonlinear denoising, IEEE Trans. Image Proc., Vol 10, 231–241. [4] Frost V.S, Stiles J.A, Shanmugam K.S, Holtzman J.C,2002, A model for radar image & its application To Adaptive digital filtering for multiplicative noise, IEEE Transaction on Pattern Analysis and Machine Intelligence, pp 175-165 . [5] Garcia R., Deriche. R, Rousson. M, Lopez. C.A,2005, Tensor processing for texture and colour segmentation, Scandinavian conference on image analysis SCIA., Vol 3540, pp 1117- 1127. [6] Gupta D., Anand R.S., Tyagi B., 2012, Enhancement of Medical Ultrasound Images using Non-Linear Filtering Based on Rational-Dilation Wavelet Transform, Proceedings of the World Congress on Engineering and Computer Science 2012, P.p. 134-142 [7] Kaun. D.T, Sowchauk T., Strand C., Chavel P.,2005, Adaptive noise smoothing filters for signal dependent Noise”, IEEE Transaction on pattern analysis and machine intelligence, Vol 7, pp 165-177 . [8] Lee J.S,2003, Digital image enhancement and noise filtering by using local statistics, IEEE Trans. Pattern Anal. Machine Intelligence. [9] Luqman M., Elamvazuthi I., Begam M., 2011, Enhancement of Bone Fracture Image Using Filtering Techniques, International Journal of Video& Image Processing and Netwo rk Security). [10] Nasrul H. M., Zulkarnain N., Zulkifli N. S, 2012, Ultrasound Liver Image Enhancement Using Watershed Segmentation Method, Journal of Engineering Research and Applications (IJERA), P.p. 27-33. [11] Perona P., Malik J.,2000, Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Transactions on Pattern Analysis and Machine Intelligence, PAMI, Vol 12(7),pp 629–639 . [12] Ragesh. N. K, Anil A.R, Rajesh R.,2011, Digital image denoising in medical ultrasound images: a survey, ICGST AIML-11 Conference, Dubai, UAE, pp 12-14. [13] Robiul H., Rashed A., 2011, A New Approach in Spatial Filtering to Reduce Speckle Noise International Journal of Soft Computing and Engineering (IJSCE), Volume-1, Issue-3

Amel A. Algaddal received the B.Sc. in College of Medical Radiological Science, Sudan University of Science and Technology. She has M.Sc. in medical diagnostic ultrasound from Sudan University of Science and Technology. She is working in Sebha Infertility Center, Sebha, Libya. Mommen A. Alkhir is lecturer Coleg of Medical Aplied Scien Sebha University, Libya.

Author Profile Dr. Yousif Mohamed Yousif Abdallah received the B.S., M.Sc., PhD degrees in Radiotherapy and Nuclear Medicine Technology from College of Medical Radiological Science, Sudan University of Science and Technology in 2005, 2009 and 2013 respectively. He received M.Sc. in Nuclear Medicine from National Cancer Institute, University of Gazeria in 2014. During 2006 up to 2015, he is working in College of Medical Radiological Science,

Paper ID: SUB15718


2385