Dedication
This project is dedicated to my beloved parents, Who educated me and enabled me to reach at this level.
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Acknowledgment Thanks to Allah at first ,and would like to express gratitude to supervisor Dr.Banazier Ahmed Abrahim for all her help guidance, her patience valuable encouragement during this study .Also would like to express thanks to the staff members in the department of biomedical engineer-Al-Sudan University. Also, heartily thankful our teachers and friends whose support us in this project.
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Table of contents Content Dedication Acknowledgment Table of content List of table List of figure Abbreviation Abstract انًستخهض
Pg.No i ii iii vi vii x xi xii
Chapter one: introduction 1.1 1.2 1.3 1.4 1.5 1.6
General review problem of the statement General objective specific objective Methodology thesis layout
1 1 1 2 2 3
Chapter two: Theoretical Background 2.1 2.2 2.2.1 2.3 2.3.1 2.4 2.4.1 2.4.2 2.4.3 2.4.4 2.5 2.6 2.1 2.2
Waves Sound waves Categories of sound What is ultrasound? Type of ultrasound wave Ultrasound’s interaction with the medium Reflection Scattering Absorption Attenuation Imaging Techniques Ultrasound Imaging System Introduction Speckle noise in ultrasound imaging:
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4 4 5 5 5 6 8 8 9 9 10 11 13 13
2.3 2.4 2.5 2.5.1 2.5.2 2.5.2.1 2.5.2.2 2.5.2.2.1 2.5.2.2.2 2.6 2.7 2.7.1 2.7.1.1 2.7.1.2 2.7.1.3 2.7.1.4 2.7.2 2.7.2.1 2.7.2.2 2.7.3 2.7.4 2.8 2.9
Physical Properties and the Pattern of Speckle Noise Need for despeckling Speckle reduction methods Compounding methods Post-acquisition method Single scale spatial filtering method Multi scale method Wavelet based speckle reduction method Pyramid based speckle reduction method Speckle noise modeling Despeckling filter Nonlinear filtering Median filter Hybrid median filter Geometric filtering Linear scaling filter Diffusion filtering Anisotropic diffusion filtering Speckle- reducing anisotropic diffusion filtering Wavelet filtering Total variation denising Limitation of despeckle filtering techniques Image quality evaluation metrics
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22 22 24 25 26 26 27 29 29 30 31
Chapter three: literature review Chapter four: materials and methodology 4 4.1 4.2 4.2.1 4.2.2 4.2.3
materials and method description First proposed method (MHMF) Second proposed method (SRAD HMF) Wavelet transform Wavelet decomposition Wavelet decomposition based SRAD method for US image by using SRAD hybrid median filter
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35 35 36 36 37 41
Chapter five: result and desiccation 5.1 5.2 5.3
Experimental result First proposed method (MHMF) Second proposed method (SRAD HMF)
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Chapter six: conclusion and future work 6.1 6.2
Conclusion Recommendation
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List of table No of table Table6.1 Table6.2
Table6.3
Table6.4
Table6.5
Table6.6
Table6.7
Table6.8
Table6.9
Table6.10
Table6.11
Table6.12
Title Image quality evaluation metrics computed for the fetal (σn =0.05) at statistical measurement of PSNR, SNR and SSIM for different filter types and for MHMF Image quality evaluation metrics computed for the fetal (σn =0.5)at statistical measurement of PSNR ,SNR and SSIM for different filter types and for MHMF Image quality evaluation metrics computed for the liver (σn =0.05)at statistical measurement of PSNR, SNR and SSIM for different filter types and for MHMF Image quality evaluation metrics computed for the liver (σn=0.5)at statistical measurement of PSNR, SNR and SSIM for different filter types and for MHMF. Image quality evaluation metrics computed for the vagina (σn =0.05)at statistical measurement of PSNR ,SNR and SSIM for different filter types and for MHMF Image quality evaluation metrics computed for the vagina (σn =0.5)at statistical measurement of PSNR, SNR and SSIM for different filter types and for MHMF Image quality evaluation metrics computed for the fetal (σn =0.05)at statistical measurement of RMSE, PSNR ,SNR and SSIM for different filter types and for SRAD HMF Image quality evaluation metrics computed for the fetal (σn =0.5)at statistical measurement of RMSE, PSNR ,SNR and SSIM for different filter types and for SRAD HMF Image quality evaluation metrics computed for the liver (σn =0.05)at statistical measurement of RMSE, PSNR ,SNR and SSIM for different filter types and for SRAD HMF Image quality evaluation metrics computed for the liver (σn =0.5)at statistical measurement of RMSE, PSNR ,SNR and SSIM for different filter types and for SRAD HMF Image quality evaluation metrics computed for the vagina (σn =0.05) at statistical measurement of RMSE, PSNR ,SNR and SSIM for different filter types and for SRAD HMF Image quality evaluation metrics computed for the vagina (σn =0.5) at statistical measurement of RMSE, PSNR ,SNR and SSIM for different filter types and for SRAD HMF vi
List of figure No. of figure
Title
Figure2.1
Sound wave propagate
No .of page 7
Figur2.2
Interaction of Ultrasound with Tissue
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Figure 2.3
Block diagram of ultrasound imaging system.
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Figure 4.1
The usual tissue model in ultrasound imaging
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Figure 4.2
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Figure 5.1 Figure 5.2
Diagram of neighborhood pixels used in the Hybrid Median Filter. Algorithm of modified Hybrid Median Filter Wavelet Transform on a signal
Figure 5.3
One level image decomposition by using DWT
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Figure 5.4
Wavelet decomposition of the fetal image noise =0.05 and 0.5
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Figure 5.5
Wavelet decomposition of the liver image noise =0.05 and 0.5
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Figure 5.6
Wavelet decomposition of the vagina image noise =0.05 and 0.5
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Figure 5.7
Block diagram of the proposed method (SRAD HMD)
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Result of First Proposed Method Figure 6.1 Figure 6.2
Results of fetal despeckled by various filter on multiplication noise (σn=0.05) Performance analysis graph to image quality evaluation metric for fetal image (noise σn =0.05).
Figure 6.3
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Results of fetal despeckled by various filter on multiplication noise (σn=0.5) Performance analysis graph to image quality evaluation metric for fetal image (noise σn =0.5).
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Figure 6.5
Results of liver despeckled by various filter on multiplication noise (σn=0.05)
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Figure 6.6
Performance analysis graph to image quality evaluation metric for liver image (noise σn =0.05)
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Figure 6.4
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Figure 6.7
Results of liver despeckled by various filter on multiplication
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noise (σn=0. 5) Figure 6.8 Figure 6.9 Figure 6.10 Figure 6.11 Figure 6.12 Figure 6.13
Figure 6.14
Figure 6.15 Figure 6.16 Figure 6.17 Figure 6.18 Figure 6.19 Figure 6.20 Figure 6.21 Figure 6.22
Performance analysis graph to image quality evaluation metric for liver image (noise σn =0.5) Results of vagina despeckled by various filter on multiplication noise (σn=0.05)
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Performance analysis graph to image quality evaluation metric for vagina image (noise σn =0.05). Results of vagina despeckled by various filter on multiplication noise (σn=0.5) Performance analysis graph to image quality evaluation metric for vagina image (noise σn =0.5) (a),(b) images filtered by hybrid median filter and modified hybrid median filter, respectively from speckled fetal image with variance (σn=0.05) (a),(b) images filtered by hybrid median filter and modified hybrid median filter, respectively from speckled liver image with variance (σn=0.5) Result of Second Proposed Method
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Results of fetal despeckled by various filter on multiplication noise (σn=0.05) Performance analysis graph to image quality evaluation metric for fetal image (noise σn =0.05) Results of fetal despeckled by various filter on multiplication noise (σn=0.5) Performance analysis graph to image quality evaluation metric for fetal image (noise σn =0.5) Results of liver despeckled by various filter on multiplication noise (σn=0.05). Performance analysis graph to image quality evaluation metric for liver image (noise σn =0.05). Results of liver despeckled by various filter on multiplication noise (σn=0.5) Performance analysis graph to image quality evaluation metric for liver image (noise σn =0.5).
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Figure 6.23 Figure 6.24
Figure 6.25 Figure 6.26 Figure 6.27
Figure 6.28
Results of vagina despeckled by various filter on multiplication noise (σn=0.05) Performance analysis graph to image quality evaluation metric for vagina image (noise σn =0.05)
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Results of vagina despeckled by various filter on multiplication noise (σn=0.5) Performance analysis graph to image quality evaluation metric for vagina image (noise σn =0.5). (a),(b),(c) images filtered by hybrid median filter and SRAD and SRAD hybrid median filter respectively from speckled fetal image with variance (σn=0.5). (a),(b),(c) images filtered by hybrid median filter and SRAD and SRAD hybrid median filter respectively from speckled vagina image with variance (σn=0.05).
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Abbreviation US US B-mode US A-mode US M-mode SND FFS NRLR NRSR PDF DsFgf4d DsFls SRAD PDE TVD MSE RMSE SNR PSNR SSIM DWT IDWT MHMF
Ultrasound Ultrasound Brightness mode Ultrasound Amplitude mode Ultrasound Motion mode Scatter Number Density Fully Formed Speckle pattern Non Randomly distributed with Long-Range order Non Randomly distributed with Short-Range order Probability Density Function Geometric Filter Linear scaling gray level filter Speckle reduction anisotropic diffusion Partial Differential Equation Total Variation Denoising Filters Mean Square Error Root Mean Square Error Signal to Noise Ratio Peak Signal to Noise Ratio Structural Similarity Index Discreet Wavelet Transform Inverse Discreet Wavelet Transform Modified Hybrid Median Filter
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Abstract Ultrasound imaging is a widely used medical tool that can help physician evaluate ,diagnose and treat medical conditions and, due to its noninvasive nature, low cost and capability of forming real time imaging it is widely used technique and considered very safe due to non ionizing radiation properties unlike X-ray .the ultrasound image shows granular appearance called speckle ,this noise decrease the human ability to identify pathological from normal tissue because it depends on the structure of the image tissue and various imaging parameters. The main purpose for speckle reduction in medical ultrasound imaging to improve the human into keep in mind that certain speckle contains diagnostic information and should be retained. The objective of this thesis is to give an overview about types of speckle reduction techniques in ultrasound imaging and to present tow new techniques of speckle reduction. And to carry out comparative evaluation of despeckle filtering based on image quality evaluation metrics. A new speckle suppression methods and coherence enhancement of medical ultrasound images where proposed: The first one is modified of hybrid median filter, the second one is combining of SRAD with hybrid median filter. It has been found that quality evaluation metrics the proposed methods performed better than all other methods while the structural details and results preserved edges and features in a better way than other despeckling filters.
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المستخلص خٓاس انًٕخاخ انظٕتٛح ْٕأداج طثٛح يستخذيح تظٕرج ٔاسؼح ف ٙانًدال انطث , ٙزتٗ تساػذ األخظائٙ ػهٗ تقٛٛى ,تشخٛض ٔػالج انساالخ انطثٛح ,ألٌ خٓاس انًٕخاخ انظٕتٛح غٛز يؤثز ػهٗ االَسدح ٔ ,قهٛم انتكهفح ًٔٚكُّ اػطاء طٕرج زٛح ف ٙانشيٍ ْ,ذِ االسثاب خؼهتّ شائغ االستخذاو ٔٚؼتثز خٓاس آيٍ ألَّ الٚستٕ٘ ػهٗ أشؼح يؤُٚح يثم خٓاس األشؼح انسُٛٛح. طٕرج انًٕخاخ انظٕتٛح تظٓز فٓٛا زثٛثاخ تسًٗ انزقطح ْ ,ذِ انسثٛثاخ تؼتثز ضٕضاءٔ ,تؤثز تذٔرْا ف ٙيقذرج األخظائ ٙػهٗ تسذٚذ ٔتًٛش األَسدح انسهًٛح يٍ األَسدح انًظاتح ,ألَٓا تزتثظ تتفاطٛم انظٕرج ٔػٕايم تظٕٚزٚح أخزٖ . انسثة األساس ٙنضزٔرج انتخهض يٍ ضٕضاء انزقطح ْٕ تسس ٍٛيقذرج األخظائ ٙػهٗ تفسٛز انظٕرج. انٓذف يٍ ْذِ األطزٔزح ْٕ إػطاء فكزج ػايح ػٍ إَٔاع تقُٛاخ انسذ يٍ ضٕضاء انزقطح ف ٙخٓاس انًٕخاخ انظٕتٛح ٔأٚضا اقتزاذ تقُٛت ٍٛف ٙانسذ يٍ ضٕضاء انزقطح ٔ ,أٚضا إخزاء يقارَح تقًٛٛٛح تٍٛ يزشساخ انسذ يٍ ضٕضاء انزقطح تُاء ػهٗ يقاٛٚس تقٛٛى خٕدج انظٕرج. انظٕت ٙف ٙانًدال ج تى اقتزاذ تؼض انطزق اندذٚذج نهسذ يٍ ضٕضاء انزقطح ٔ تؼشٚش طٕر انًٕخاخ انطث ْٙ ٔ ، ٙػهٗ انُسٕ انتان.ٙ أٔال :تؼذٚم يزشر انٓد ٍٛانًتٕسظ ,ثاَٛا :ديح يزشر انسذ يٍ ضٕضاء انزقطّ يتثاُٚح انخٕاص يغ يزشر انٓد ٍٛانًتٕسظ. ٔقذ تٕطمَا فْ ٙذا انثسث أٌ انطزٚقت ٍٛانًقتززت ْٙ ٍٛاألفضم يٍ زٛث يقاٛٚس تقٛٛى خٕدج انظٕرج, ألَٓا تسافظ ػهٗ تفاطٛم انظٕرج ٔانُتائح انٓٛكهٛح انسٕاف ٔانًالير تشكم أفضم يقارَح تانطزق األخزٖ نًزشساخ اسانح ضٕضاء انزقطح.
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