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Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects’ optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region

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Abstract

This study proposes a novel combined filter accompanied with different optimization algorithms for Poisson noise reduction and increases image quality in digital X-ray and CT images. This filter uses 4th-order PDE, TV, Bayes shrink threshold with optimization algorithms and an exact unbiased inverse of generalized Anscombe transformation (EUIGAT). Experiments were conducted on the basis of displaying the influence of denoising filter on 105 simulated, 102 radiographic and 102 CT images of individuals aged 20–70 years old; 53 men and 49 women. Experimental results demonstrated the lowest value for MSE and the highest values for PSNR, IQI, SSIM, FOM and CNR in different kinds of kernels and images compared with the other fuzzy Bio-inspired algorithms. The results showed proposed method helps physicians and orthopedists in order to enhance their performances in treating injuries of the pelvic region such as acetabulum fossa and head and neck femur bone.

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References

  1. Available at http://the natural image noise dataset is published on wiki-media commons (https://commons.wikimedia.org/wiki/Natural_Image_Noise_Dataset)

  2. Abdulameer AT (2018) An improvement of MRI brain images classification using dragonfly algorithm as trainer of artificial neural network. Ibn A-Haitham J for Pure & Appl Sci 31:268–276

    Google Scholar 

  3. Abualigah L, Diabat A, Sumari P, Gandomi AH (2021) A novel evolutionary arithmetic optimization algorithm for multilevel thresholding segmentation of covid-19 ct images. Processes 9(7):1155

    Google Scholar 

  4. Ahirwal MK, Kumar A, Singh GK (2014) Adaptive filtering EEG/ERP through bounded range artificial bee colony (BR-ABC) algorithm. Digital Signal Process 25:164–172

    Google Scholar 

  5. Al Shalabi L, Shaaban Z, Kasasbeh B (2006) Data mining: A preprocessing engine. J Comput Sci 2:735–739

    Google Scholar 

  6. Alzyoud K, Hogg P, Cert MPG, Snaith B, Flintharn K, England A (2018) Optimum positioning for anteroposterior pelvis radiography: A literature review. J Med Imaging Radiat Sci 49:316–324

    Google Scholar 

  7. Anoop V, Bipin PR (2019) Medical image enhancement by a bilateral filter using optimization technique. J Med Syst 43:1–12

    Google Scholar 

  8. Antam R (2014) Performance analysis of image denoising with wavelet thresholding methods for different levels of decomposition. Int J Multimed Appl (IJMA). 6:35–46

  9. Arora S, Singh S (2018) Butterfly optimization algorithm: a novel approach global optimization. Soft Computing 1–21

  10. Arvindan TE, Seshasayanan R (2018) Denoising brain images with the aid of discrete wavelet transform and monarch butterfly optimization with different noises. J Med Syst 42:1–13

    Google Scholar 

  11. Borges LR, Oliveria HCR, Nunes PF, Bakic PR, Maidment ADA, Vieira MAC (2016) Method for simulation dose reduction in digital mammography using Anscombe transformation. Med Phys 43:2704–2714

    Google Scholar 

  12. Cao X, Miao J, Xiao Y (2017) Medical image segmentation of improved genetic algorithm research based on dictionary learning. World J Eng Technol 5:90–96

    Google Scholar 

  13. Chan TF, Osher S, Shen J (2000) The digital TV filter and nonlinear denoising. IEEE Trans Image Progressing 10:231–241

    Google Scholar 

  14. Chauhan N, Choi BJ (2018) Performance analysis of denoising algorithms for human brain image. Int J Fuzzy Logic Intell Syst 18:175–181

    Google Scholar 

  15. Chetan S, Shesshadri HS, Lokesha V (2017) Hybrid algorithm edge detected DICOM image enhancement and analysis based on genetic algorithm for evolution and best fit value. J Biomed Eng Med Imaging 4:1–11

    Google Scholar 

  16. Chikhalekar AT (2016) Analysis of image processing for digital X-ray. Int Res J Eng Technol 3:1364–1368

    Google Scholar 

  17. Choukri D, Mehdi A (2017) A new predictive approach to variables selection through genetic algorithm and fuzzy adaptive resonance theory using medical diagnosis as a case. The 8th International Conference on Ambient Systems, Networks and Technologies (ANT2017). 448–57

  18. Dahov K, Foi A, Katkovnik V, Egiazarian K. (2006) Image denoising with block-matching and 3D filtering. In Image processing: algorithms and systems, neural networks, and machine learning 606414(206):1–14

  19. Das S, Saha B (2009) Data quality y mining using genetic algorithm. Int J Comput Sci Secur 3:105–112

    Google Scholar 

  20. Djenouri Y, Djenouri D, Belhadi A, Cano A (2019) Exploiting GPU and cluster parallelism in single scan frequent itemset mining. Inf Sci 496:363–377

    Google Scholar 

  21. Fan Z, Sun Q, Ji Z, Ruan F, Zhao L (2013) A image filter arithmetic based on GA, PDE, and TV. Int J Futur Gener Commun Netw 8:147–156

    Google Scholar 

  22. Fan L, Zhang F, Fan H, Zhang C (2019) Brief review of image denoising techniques. Visual Comput Industry Biomed Art 2:1–12

    Google Scholar 

  23. Fujli M, Aoki T, Okata Y, Mori H, Knoshita S, Hayashida Y et al (2016) Prediction of femoral neck strength in patients with diabetes mellitus with trabecular bone analysis tomosynthesis images. Radiology 281:933–939

    Google Scholar 

  24. Ganji MF, Saniee AM (2011) A fuzzy classification system based ant colony optimization for diabetes disease diagnosis. Expert Syst Appl 38:14650–14659

    Google Scholar 

  25. Ghosh P, Mitchell M, Tanyi A, Hung AY (2016) Incorporating priors for medical segmentation using a genetic algorithm. Neurocomputing 195:181–194

    Google Scholar 

  26. Godil SS, Shamim MS, Enam SA, Qidwari U (2011) Fuzzy logic: A “ simple” solution for complexities in neurosciences? Surg Neurol Int 9:1–9

    Google Scholar 

  27. Gopalakrishnan RC, Kuppusamy V (2014) Ant colony optimization approaches to clustering of lung nodules from CT images. Comput Math Methods Med 1–15

  28. Gudmundsson M, El-Kwae E, Kabuka MR (1998) Edge detection in medical images using a genetic algorithm. IEEE Trans Med Imaging 17:469–474

    Google Scholar 

  29. Hadayzadeh R, Salmassi FA, Akbari R, Ziarati K (2010) Termite colony optimization: A novel approach for optimizing continuous problems. 18th Iranian Conference on Electrical Engineering. 1–6

  30. Hajiaboli MR (2011) An anisotropic fourth-order diffusion filter for image noise removal. Int J Comput Vis 92:177–191

    MathSciNet  Google Scholar 

  31. Hariya Y, Kurihara T, Shindo T, Jinno K (2015) A study of robustness of PSO for non-separable evaluation functions. International Symposium Nonlinear Theory and its Applications 1:724–727

    Google Scholar 

  32. Hosseinian S, Arefi H (2016) Assessment of restoration methods of X-ray images with emphasis on medical photogrammetric usage. Int Arch Photogramm Remote Sens Spatial Inf Sci. XLI-B5:835–40

  33. Houssein EH, Emam MM, Ali AA (2021) Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images. Neural Comput Appl 33(24):16899–16919

    Google Scholar 

  34. Iravani Rad MA, Moshayedi AJ (2017) Compare and investigate the evolutionary optimization algorithms insect colonies. Congress of Mechanical Engineering 1–18

  35. Izonin I, Tkachenko R, Kryvinska N, Tkachenko P (2019) Multiple linear regression based on coefficients identification using non-iterative SGTM neural-like structure. In International Work-Conference on Artificial Neural Networks (pp. 467–479). Springer, Cham

  36. Jang JS, Yang HJ, Koo HJ, Kim SH, Park CR, Yoon SH et al (2018) Image quality assessment with dose reduction using high kVp and additional filtration for abdominal digital radiography. Physica Med 50:46–51

    Google Scholar 

  37. Janny Shabu SL, Jayakumar C (2018) Multimodal image fusion using an evolutionary based algorithm for brain tumor detection. Biomed Res 29:2932–2937

    Google Scholar 

  38. Kalyani C, Ramudu K, Reddy GR (2018) Optimized segmentation of tissues and tumors in medical images using AFMKM clustering via level set formulation. Int J Appl Eng Res 13:4989–4999

    Google Scholar 

  39. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697

    Google Scholar 

  40. Karthikeyan K, Chandrasekar C (2011) Speckle noise reduction of medical ultrasound images using Bayesshrink wavelet threshold. Int J Comput Appl 2(2):8–14

    Google Scholar 

  41. Kaur S, Singh I (2016) Comparison between edge detection techniques. Int J Comput Appl 145:15–17

    Google Scholar 

  42. Kaushik P, Jain M, Jain A (2018) A pixel-based digital medical images protection using genetic algorithm. Int J Electron Commun Eng 11:31–37

    Google Scholar 

  43. Khan KB, Khliq AA, Shahid M, Ulah H (2016) Poisson noise reduction in scintigraphic images using gradient adaptive trimmed mean filter. International Conference on Intelligent systems Engineering. 301–305

  44. Khanian M, Feizi A, Davari A (2014) An optimal partial differential equations-based stopping criterion for medical denoising. J Med Signals Sensors 4:72–83

    Google Scholar 

  45. Khmag A, Ramli AR, Hashim SJ, Al-Haddad SAR (2013) Review of image denoising algorithms based on the wavelet transformation. Int J Adv Trends Comput Sci Eng (IJATCSE) 2:1–7

    Google Scholar 

  46. Khursheed S, Khaliq AA, Shah JA, Abdullah S, Khan S (2014) A hybrid logarithmic gradient algorithm for Poisson noise removal in medical images. Adv Stud Biol 6:181–192

    Google Scholar 

  47. Kiriti T, Jitendra K, Ashok S (2017) Poisson noise reduction from X-ray by region classification and response median filtering. Sadhana 42:855–863

    MathSciNet  Google Scholar 

  48. Kiruthigha K, Ravichandran (2017) A survey on fruit fly optimization algorithm and its improvement. Res J Pharm Biol Chem Sci 8:757–767

    Google Scholar 

  49. Kockanat S, Karaboga N (2013) Parameter tuning of artificial bee colony algorithm for Gaussian noise elimination on digital images. IIEEE International Symposium on Innovations in Intelligent Systems and Applications 1–4

  50. Kumar M, Kishor A, Abawajy J, Agarwal P, Singh A, Zomaya A (2021) ARPS: An autonomic resource provisioning and scheduling framework for cloud platforms. IEEE Trans Sustain Comput

  51. Kumar N, Kumar S (2010) Image quality assessment techniques. Int J Adv Res Comput Sci Softw Eng 3:636–640

    Google Scholar 

  52. Kumar M, Sharma SC (2020) PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing. Neural Comput Appl 32:12103–12126

    Google Scholar 

  53. Kumar M, Sharma SC (2020) PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing. Neural Comput Appl 32(16):12103–12126

    Google Scholar 

  54. Kumar M, Sharma SC, Goel A, Singh SP (2019) A comprehensive survey for scheduling techniques in cloud computing. J Netw Comput Appl 143:1–33

    Google Scholar 

  55. Lambora A, Gupta K, Chopra K (2019) Genetic algorithm - A literature review. International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-Con) 380–4

  56. Lee S, Lee MS, Kang MG (2018) Poisson-Gaussian noise analysis and estimation for low dose X-ray images the NSCT domain. Sensors 18:1–22

    Google Scholar 

  57. Lee SJ, Park YS (2015) Plain radiography of the hip: a review of radiographic techniques and image features. Hip & pelvic 27:125–134

    Google Scholar 

  58. Lee WA, Saroki AJ, Løken S, Trindade CA, Cram TR, Schindler BR, Philippon MJ (2016) Radiographic identification of arthroscopically relevant acetabular structures. Am J Sports Med 44(1):67–73

    Google Scholar 

  59. Li M, Du W, Nian F (2014) An adaptive particle swarm optimization algorithm based on directed weighted complex network. Math Probl Eng 1–7

  60. Li Y, Lu J, Wang L, Yahagi T (2007) Noise removal for degraded images with Poisson noise using M -Transformation and Bayesshrink method. Electr Comm Jpn 90:508–512

    Google Scholar 

  61. Li Y, Niu M, Guo J (2019) An inductive logic programming algorithm based on artificial bee colony. J Inf Technol Res (JITR) 12:89–104

    Google Scholar 

  62. Love LA, Kruger RA (1987) Scatter estimation for a digital radiographic using convolution filtering. Med Physics 14:178–185

    Google Scholar 

  63. Luisier F, Blu T, Unser M (2011) Image denoising in mixed Poisson-Gaussian noise. IEEE Trans Image Process 20:696–708

    MathSciNet  Google Scholar 

  64. Maini R, Aggarwal H (2009) Study and comparison of various image edge detection techniques. Int J Image Process 3:1–12

    Google Scholar 

  65. Makitalo M, Foi A (2010) Optimal inversion of the Anscombe transformation in low-count Poison image denoising. IEEE Trans Image Process 20:99–108

    Google Scholar 

  66. Makitalo M, Foi A (2013) Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise. IEEE Trans Image Process. 22:91–103

    MathSciNet  Google Scholar 

  67. Mirjalili S (2015) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 1–21

  68. Muniyappan S, Rajendran P (2019) Contrast enhancement of medical images through adaptive genetic algorithm (AGA) over genetic algorithm and particle swarm optimization. Multimed Tools Appl 78:6487–6510

    Google Scholar 

  69. Nadernejad E, Koohi H, Hassanpour H (2008) PDEs-based method for image enhancement. Appl Math Sci 2:981–993

    MathSciNet  Google Scholar 

  70. Nair R, Bhagat A (2018) A life cycle on processing large dataset-LCPL. Int J Comput Appl 179:27–34

    Google Scholar 

  71. Nie Q, Zou YB, Lin JCW (2021) Feature extraction for medical ct images of sports tear injury. Mobile Netw Appl 26(1):404–414

    Google Scholar 

  72. Nikpour M, Hassanpour H (2010) Using diffusion equations for improving performance of wavelet-based image denoising techniques. IET Image Process 4:452–462

    MathSciNet  Google Scholar 

  73. Ostojic VS, Starcevic DS, Petrovic VS (2018) Recursive noise reduction of digital radiography images. Telfor Journal 10:26–31

    Google Scholar 

  74. Oulhaj H, Amine A, Rziza M, Ajdine A (2012) Noise reduction in medical images-comparison of noise removal algorithms. International Conference on Multimedia Computing and Systems. 1–6

  75. Pan QK, Sang HY, Duan JH, Gao L (2014) An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl-Based Syst 62:69–83

    Google Scholar 

  76. Pan X, Xue L, Li R (2019) A new and efficient firefly algorithm for numerical optimization problems. Neural Comput Appl 31:1445–1453

    Google Scholar 

  77. Parpinelli RS, Lopes HS, Freitas AA (2002) Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput 6:321–332

    Google Scholar 

  78. Pereira DC, Ramos RP, do Nascimento MZ (2014) Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed. 114:88–101

    Google Scholar 

  79. Rafati M, Arabfard M, Rafati Rahimzadeh M, Maghsoudloo M (2016) Assessment of noise reduction in ultrasound images of common carotid and brachial arteries. IET Comput Vis 10:1–8

    Google Scholar 

  80. Rafati M, Arabfard M, Rafati Rahimzadeh M, Voshtani H, Moladoust H (2015) A comparative study of three speckle reducing methods for intima-media thickness ultrasound images. Iran Red Crescent Med J 17:1–7

    Google Scholar 

  81. Rafati M, Arabfard M, Rafati-Rahimzadeh M (2014) Comparison of different edge detections and noise reduction on ultrasound images of carotid and brachial arteries using a speckle reducing anisotropic diffusion filter. Iran Red Crescent Med J 16:1–9

    Google Scholar 

  82. Rafati M, Farnia F, Erfanian Taghvaei M, Nickfarjam AM (2018) Fuzzy genetic-based noise removal filter for digital panoramic X-ray images. Biocybern Biomed Eng 38:941–965

    Google Scholar 

  83. Ragesh NK, Anil AR, Rajesh R (2011) Digital image denoising in medical ultrasound images: A survey. ICGST AIML-11 Conference Dubai, UAE. 12:67–73

  84. Rahman CM, Rashid TA (2019) Dragonfly algorithm and its applied science survey. Comput Intell Neurosc. 1–21

  85. Rosenfeld A, Pfaltz JL (1966) Sequential operations in digital picture processing. J ACM (JACM) 13:471–497

    Google Scholar 

  86. Rudin LI, Osher S, Fatemi E (1999) Nonlinear total variation based noise removal algorithms. Physica D 60:259–268

    MathSciNet  Google Scholar 

  87. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimization algorithm: Theory and application. Adv Eng Softw 105:30–47

    Google Scholar 

  88. Semchedine M, Moussaoui A (2017) An efficient particle swarm optimization for MRI fuzzy segmentation. Romanian J Inf Sci Technol 20:271–285

    Google Scholar 

  89. Sharma S, Malik A (2016) Termite colony optimization based routing in wireless mesh networks. Int Res Adv 3:56–61

    Google Scholar 

  90. Shen W, Xu W, Zhang H, Sun Z, Ma J, Ma X, ... & Wang Y (2021) Automatic segmentation of the femur and tibia bones from X-ray images based on pure dilated residual U-Net. Inverse Probl Imaging 15(6); 1333

  91. Simone G, Audino G, Farup I, Albregtsen F, Rizzi A (2014) Termite retinex: a new implementation based on a colony of intelligent agents. J Electron Imaging 23:1–13

    Google Scholar 

  92. Sivakumar R, Gayathri MK, Nedumaran D (2010) Speckle filtering of ultrasound B-mode images- A comparative study single scale spatial adaptive filters, Multiscale filter and diffusion filters. IACSIT Int J Eng Technol 2:514–523

    Google Scholar 

  93. Stem ES, Oconner MI, Kransdorf MJ, Crook J (2006) Computed tomography analysis of acetabular anteversion and abduction. Skeletal Radiol 3005:385–389

    Google Scholar 

  94. Stolojescu-Crisan C, Holban S (2013) A comparison of X-ray image segmentation techniques. Adv Electr Comput Eng 13:85–92

    Google Scholar 

  95. Sun L, Chen S, Xu J, Tian Y (2015) Improved monarch butterfly optimization algorithm based on opposition-based learning and random local perturbation. Complexity. 1–20

  96. Thakur KV, Damodare OH, Sapkal AM (2016) Poisson reducing unilateral filtering for X-ray image denoising. International Conference on Communication Computing Visualization. 9–13

  97. Thanh DNH, Prasath VBS, Hieu LM (2019) A review on CT and x-ray images denoising methods. Informatica 43:151–159

    MathSciNet  Google Scholar 

  98. Varan CS, Jagan A, Kaur J, Joti D, Rao DS (2011) Image quality assessment techniques pn spatial domain. Int J Comput Sci Technol 2:177–184

    Google Scholar 

  99. Veeramuthu A, Meenakshi S (2017) Breeding firefly associations rules for effective medical image retrieval. Biomedical Research (2017) Artificial Intelligent Techniques for Bio Medical Signal Processing: Edition-I 152–157

  100. Wang L, Lu J, Li Y, Yahagi T, Okamoto T (2008) Noise removal for medical X-ray images in wavelet domain. Electr Eng Jpn 163:37–46

    Google Scholar 

  101. Wang Z, Sheik HR, Bovik AC (2002) No-reference perceptual quality assessment of JPEG compressed images. Pro ICIPo2. 1: 477–480

  102. Wang X, Wong BS, Tui CG (2004) X-ray image segmentation based on genetic algorithm and maximum fuzzy entropy. Proceeding of 2004 IEEE Conference on Robotic, Automation, Mechatronics Singapore. 991–995

  103. Xue W (2021) UNet-based Fully-automatic Segmentation of the Capitate from CT Images

  104. Yang XS, He XS (2018) Why the fire fly algorithm works? Springer, Cham, pp 245–259

    Google Scholar 

  105. Yousif Aballah YM, Abdelwahab RI (2014) Improvement of orthopantomography (OPG) images using texture analysis. Int J Sci Res 3:1771–1775

    Google Scholar 

  106. Yousif Aballah YM, Almoustafa AA, Elhadi G, Mohammed M, Khalafallah O, Khalid T (2011) Application of analysis approach in noise estimation in panoramic X-ray images using image processing program (Matlab). Can J Med 2:38–48

    Google Scholar 

  107. Zhang Y (2015) Research on X-ray image enhancement technology based on fruit fly optimization algorithm. Metall Mining Indust. 745–751

  108. Zhao R, Ni H, Feng H, Song Y, Zhu X (2019) An improved grasshopper optimization algorithm for task scheduling problems. Int J Innov Comput Inf Control 15:1967–1987

    Google Scholar 

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Rafati, M., Kalantari, N., Azadbakht, J. et al. Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects’ optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region. Multimed Tools Appl 83, 15483–15531 (2024). https://doi.org/10.1007/s11042-023-15341-w

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