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A new algorithm for removing salt and pepper noise from color medical images

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Abstract

A new method for eliminating salt and pepper noise from color medical images is formulated in this work. The presence of noise in the medical images degrades image quality, affecting disease analysis, detection, and diagnosis by the doctors. Therefore, removal of noise from the medical image is crucial. For color image, vector median filter is preferred for decreasing presence of salt and pepper noise as it preserves the correlation between the channels. However, applying filter on the image without detecting the noise not only reduces noise, but also produces blurring effect in the homogeneous regions and removes the important features such as textures, edges, thin lines, curves, corners etc. presence in the images. This paper proposes a switching vector median filter that detects the salt and pepper noise in the images prior to the filtering operation to avoid such undesirable effects. The vector median filter is applied in the filtering kernel if the central vector pixel does not lie in the set of healthy vector pixels and the minimum average sums of the distances of the vector pixels that forms the edges in the four directions is more than a predetermined threshold. In comparison to existing common filters, the simulation results demonstrate the proposed filter’s superior performance for color medical image in decreasing salt and pepper noise and maintaining details.

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Data availability

The dataset analyzed during the current study are available in the web links https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-019-4121-7, https://challenge.isic-archive.com/data/, and https://sipi.usc.edu/database/.

References

  1. Aksac A, Demetrick DJ, Ozyer T, Alhajj R (2019) BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis. BMC Res Notes 12(1):1–3

    Article  Google Scholar 

  2. Aldhyani TH, Nair R, Alzain E, Alkahtani H, Koundal D (2022) Deep learning `model for the detection of real time breast cancer images using improved dilation-based method. Diagnostics 12(10):2505

    Article  Google Scholar 

  3. Arora S, Hanmandlu M, Gupta G (2020) Filtering impulse noise in medical images using information sets. Pattern Recogn Lett 139:1–9

    Article  Google Scholar 

  4. Astola J, Haavisto P, Neuvo Y (1990) Vector median filters. Proc IEEE 78:678–689

    Article  Google Scholar 

  5. Cao Y, Fu Y, Zhu Z, Rao Z (2021) Color random valued impulse noise removal based on quaternion convolutional attention Denoising network. IEEE Signal Process Lett 29:369–373

    Article  Google Scholar 

  6. Celebi ME, Schaefer G (2013) Color medical image analysis/M. Emre Celebi, Gerald Schaefer, editors. Dordrecht: Springer

  7. Chanu PR, Singh K (2018) Impulse noise removal from medical images by two stage quaternion vector median filter. J Med Syst 42(10):1–10

    Article  Google Scholar 

  8. Chanu PR, Singh KH (2019) A two-stage switching vector median filter based on quaternion for removing impulse noise in color images. Multimed Tools Appl 78(11):15375–15401

    Article  Google Scholar 

  9. Chen P, Huang S, Yue Q (2022) Skin lesion segmentation using recurrent attentional convolutional networks. IEEE Access 10:94007–94018

    Article  Google Scholar 

  10. Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) 168-172.

  11. Gao J, Du Z, Shi Z, Xu Z, Cao Q, Tang R (2018) Switching impulse noise filter based on Laplacian convolution and pixels grouping for color images. SIViP 12(8):1523–1529

    Article  Google Scholar 

  12. HosseinKhani Z, Hajabdollahi M, Karimi N, Soroushmehr R, Shirani S, Najarian K, Samavi S (2018) Adaptive real-time removal of impulse noise in medical images. J Med Syst 42(11):1–9

    Article  Google Scholar 

  13. Jin L, Li D (2008) Improved directional-distance filter. Front Mech Eng China 3(2):205–211

    Article  Google Scholar 

  14. Jin L, Song E, Zhang W (2020) Denoising color images based on local orientation estimation and CNN classifier. J Math Imaging Vision 62(4):505–531

    Article  MathSciNet  MATH  Google Scholar 

  15. Karvelis PS, Fotiadis DI (2007) Enhancement of multispectral chromosome image classification using vector median filtering. InICCS 2007 49-54 Springer, London

  16. Kashyap R (2022) Stochastic dilated residual ghost model for breast cancer detection. J Digital Imaging:1–12

  17. Li C, Li J, Luo Z (2021) An impulse noise removal model algorithm based on logarithmic image prior for medical image. SIViP 15(6):1145–1152

    Article  Google Scholar 

  18. Liu Z, Xiong R, Jiang T (2022) CI-net: clinical-inspired network for automated skin lesion recognition. IEEE Trans Med Imaging:1

  19. Lukac R (2003) Adaptive vector median filtering. Pattern Recogn Lett 24(12):1889–1899

    Article  Google Scholar 

  20. Lukac R, Plataniotis KN (2006) A taxonomy of color image filtering and enhancement solutions. Advances Imaging Electron Phys 140:188

    Google Scholar 

  21. Lukac R, Smołka B (2003) Application of the adaptive center-weighted vector median framework for the enhancement of cDNA microarray images. Int J Appl Math Comput Sci 13(3):369–383

    MathSciNet  MATH  Google Scholar 

  22. Malinski L, Smolka B (2016) Fast averaging peer group filter for the impulsive noise removal in color images. J Real-Time Image Proc 11(3):427–444

    Article  Google Scholar 

  23. Meng X, Lu T, Min F, Lu T (2021) An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation. IET Image Process 15(1):228–238

    Article  Google Scholar 

  24. Oistamo K, Liu Q, Grundstrom M, Neuvo Y (1992) Weighted vector median operation for filtering multispectral data. In[proceedings 1992] IEEE International Conference on Systems Engineering. 16-19

  25. Radlak K, Malinski L, Smolka B (2019) Deep learning for impulsive noise removal in color digital images. Real-Time Image Process Deep Learn 10996:18–26

    Google Scholar 

  26. Radlak K, Malinski L, Smolka B (2020) Deep learning based switching filter for impulsive noise removal in color images. Sensors 20(10):2782

    Article  Google Scholar 

  27. Rastegar H, Giveki D (2022) Designing a new deep convolutional neural network for skin lesion recognition. Multimed Tools Appl:1–17

  28. Roy A, Singha J, Manam L, Laskar RH (2017) Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images. IET Image Process 11:352–361

    Article  Google Scholar 

  29. Roy A, Manam L, Laskar RH (2020) Removal of ‘salt & pepper ‘noise from color images using adaptive fuzzy technique based on histogram estimation. Multimed Tools Appl 79(47):34851–34873

    Article  Google Scholar 

  30. Sadrizadeh S, Otroshi-Shahreza H, Marvasti F (2022) Impulsive noise removal via a blind CNN enhanced by an iterative post-processing. Signal Process 192:108378

    Article  Google Scholar 

  31. Sheela CJ, Suganthi G (2020) An efficient denoising of impulse noise from MRI using adaptive switching modified decision based unsymmetric trimmed median filter. Biomed Signal Process Control 55:101657

    Article  Google Scholar 

  32. Singh KM, Bora PK (2014) Switching vector median filters based on non-causal linear prediction for detection of impulse noise. Imaging Sci J 62(6):313–326

    Article  Google Scholar 

  33. Singh I, Verma OP (2021) Impulse noise removal in color image sequences using fuzzy logic. Multimed Tools Appl 80(12):18279–18300

    Article  Google Scholar 

  34. Smolka B, Chydzinski A (2005) Fast detection and impulsive noise removal in color images. Real-Time Imaging 11(5–6):389–402

    Article  Google Scholar 

  35. Smolka B, Malik K, Malik D (2015) Adaptive rank weighted switching filter for impulsive noise removal in color images. J Real-Time Image Proc 10(2):289–311

    Article  Google Scholar 

  36. Trahanias PE, Venetsanopoulos AN (1993) Vector directional filters-a new class of multichannel image processing filters. IEEE Trans Image Process 2(4):528–534

    Article  Google Scholar 

  37. Wang G, Li D, Pan W, Zang Z (2010) Modified switching median filter for impulse noise removal. Signal Process 90(12):3213–3218

    Article  MATH  Google Scholar 

  38. Weber AG (2006) The USC-SIPI image database: version 5. http://sipi.USC.Edu/database/ (last accessed on 01/06/2022).

  39. Yoo Y, Lee S, Choe W, Kim CY (2007) CMOS image sensor noise reduction method for image signal processor in digital cameras and camera phones. Digital Photography III 6502:263–272

    Google Scholar 

  40. Zhang W, Jin L, Song E, Xu X (2019) Removal of impulse noise in color images based on convolutional neural network. Appl Soft Comput 82:105558

    Article  Google Scholar 

  41. Zhou H, Mao KZ (2008) An impulsive noise color image filter using learning-based color morphological operations. Digital Signal Process 18(3):406–421

    Article  Google Scholar 

  42. Zhou T, Li L, Bredell G, Li J, Konukoglu E (2021) Quality-aware memory network for interactive volumetric image segmentation. In International conference on medical image computing and computer-assisted intervention 560–570 Springer, Cham

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Funding

This study was funded by University Grants Commission (UGC) (No. F.15–9(JULY 2018)/2018(NET)).

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Correspondence to Thiyam Romita Chanu.

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Chanu, T.R., Singh, T.R. & Singh, K.M. A new algorithm for removing salt and pepper noise from color medical images. Multimed Tools Appl 82, 24991–25013 (2023). https://doi.org/10.1007/s11042-023-14378-1

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