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A modified unsharp masking with adaptive threshold and objectively defined amount based on saturation constraints

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Abstract

Unsharp Masking (UM) is a popular technique widely used for sharpening medical imagery. However, UM has two operational parameters which have crucial influence on its performance. They are amount (λ) and threshold (α). Improper selection of the threshold and amount will cause noise amplification and overshoot artefact, respectively. A fully adaptive UM with data driven operational parameters, for sharpening Magnetic Resonance (MR) images is proposed in this paper. The proposed sharpening scheme is compared with the homomorphic filter in terms of sharpness of the output image, width of salient edges, feature preservation, saturation and edge quality degradation due to noise. The proposed scheme of UM is found to be superior to homomorphic filter in terms of edge strength and feature preservation and it is free from overshoot artefacts. Modified configuration of UM, proposed in this paper can be used for improving the edge quality of MR images. It can be incorporated as a plug-in in software tools used for the automated analysis of MRI.

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References

  1. Alasadi AHH, Al-Saedi AKH (2017) A method for micro-calcifications detection in breast mammograms. J Med Syst:41–68

  2. Anoop BN, Joseph J, Williams J, Jayaraman JS, Sebastian AM, Sihota P (2018) A prospective case study of high boost, high frequency emphasis and two-way diffusion filters on MR images of glioblastoma multiforme. Australas Phys Eng Sci Med 41(2):415–427

    Article  Google Scholar 

  3. Brettle D, Carmichael F (2011) The impact of digital image processing artefacts mimicking pathological features associated with restorations. Br Dent J 211:167–170

    Article  Google Scholar 

  4. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low- and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002

    Article  Google Scholar 

  5. Datta E, Papinutto N, Schlaeger R, Zhu A, Carballido-Gamio J, Henry RG (2017) Grey matter segmentation of the spinal cord with active contours in MR images. NeuroImage 147:788–799

    Article  Google Scholar 

  6. Deng G (2011) A generalized unsharp masking algorithm. IEEE Trans Image Process 20(5):1249–1261

    Article  MathSciNet  Google Scholar 

  7. Deng H, Deng W, Sun X, Ye C, Zhou X (2016) Adaptive intuitionistic fuzzy enhancement of brain tumor MR images, Scientific Reports 6, Article number: 35760, Nature

  8. Fan CN, Zhang FY (2011) Homomorphic filtering based illumination normalization method for face recognition. Pattern Recogn Lett 32(10):1468–1479

    Article  Google Scholar 

  9. Feichtenhofer C, Fassold H, Schallauer P (2013) A perceptual image sharpness metric based on local edge gradient analysis 20(4):379–382

  10. Fernández SA, Ciak TP, Ferrero GVS (2015) Spatially variant noise estimation in MRI: a homomorphic approach. Med Image Anal 20(1):184–197

    Article  Google Scholar 

  11. Geng Y, Liang RZ, Li W, Wang J, Liang G, Xu C, Wang JY (2016) Learning convolutional neural network to maximize Pos@ Top performance measure, arXiv preprint arXiv:1609.08417

  12. Grigoryan AM, Dougherty ER, Agaian SS (2016) Optimal wiener and homomorphic filtration: review. Signal Process 121:111–138

    Article  Google Scholar 

  13. Guan J, Zhang W, Gu J, Ren H (2015) No-reference blur assessment based on edge modelling. J Vis Commun Image Represent 29:1–7

    Article  Google Scholar 

  14. Hajiaghayi M, Groves EM, Jafarkhani H, Kheradvar A (2017) A 3-D active contour method for automated segmentation of the left ventricle from magnetic resonance images. IEEE Trans Biomed Eng 64(1):134–144

    Article  Google Scholar 

  15. Hari VS, Jagathy Raj VP, Gopikakumari R (2013) Unsharp masking using quadratic filter for the enhancement of fingerprints in noisy background. Pattern Recogn 46(12):3198–3207

    Article  Google Scholar 

  16. İlk HG, Jane O, İlk Ö (2011) The effect of Laplacian filter in adaptive unsharp masking for infrared image enhancement. Infrared Phys Technol 54(5):427–438

    Article  Google Scholar 

  17. Ilunga-Mbuyamba E, Avina-Cervantes JG, Garcia-Perez A, de Jesus Romero-Troncoso R, Aguirre-Ramos H, Cruz-Aceves I, Chalopin C (2017) Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation. Neurocomputing 220:84–97

    Article  Google Scholar 

  18. Joseph J, Periyasamy R (2018) A fully customized enhancement scheme for controlling brightness error and contrast in magnetic resonance images. Biomed Signal Process Control 39:271–283

    Article  Google Scholar 

  19. Joseph J., Periyasamy R (2018) An image driven bilateral filter with adaptive range and spatial parameters for denoising magnetic resonance images. Electrical & Computer Engineering. https://doi.org/10.1016/j.compeleceng.2018.02.033 (In press)

  20. Joseph J, Jayaraman S, Periyasamy R, Simi VR (2017) An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images. Biocybernetics and Biomedical Engineering, Available online 20 January 2017

  21. Khadidos A, Sanchez V, Li CT (2017) Weighted level set evolution based on local edge features for medical image segmentation. IEEE Trans Image Process 26(4):1979–1991

    Article  MathSciNet  Google Scholar 

  22. Krasula L, Le Callet P, Fliegel K, Klíma M (2017) Quality assessment of sharpened images: challenges, methodology, and objective metrics. IEEE Trans Image Process 26(3):1496–1508

    Article  Google Scholar 

  23. Li Q, Zhou X, Gu A, Li Z, Liang RZ (2016) Nuclear norm regularized convolutional Max Pos@Top machine. Neural Comput Applic

  24. Liang RZ, Shi L, Wang H, Meng J, Wang JJY, Sun Q, Gu Y (2016) Optimizing top precision performance measure of content based image retrieval by learning similarity function. Proc. 23rd International Conference on Pattern Recognition (ICPR)

  25. Liang RZ, Xie W, Li W, Wang H, Wang JJY, Taylor L (2016) A novel transfer learning method based on common space mapping and weighted domain matching, proc. IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)

  26. Lin SCF, Wong CY, Jiang G, Rahman MA, Ren TR, Kwok N, Shi H, Yu Y-H, Wu T (2016) Intensity and edge based adaptive unsharp masking filter for color image enhancement. Optik 127(1):407–414

    Article  Google Scholar 

  27. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data, IJCAI

  28. Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Proc. Thirtieth AAAI Conference on Artificial Intelligence, p 201–207

  29. Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban Water Quality Prediction Based on Multi-task Multi-view Learning, Proc. Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), pp.2576–2582

  30. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  31. Panetta K, Zhou Y, Agaian S, Jia H (2011) Nonlinear unsharp masking for mammogram enhancement. IEEE Trans Inf Technol Biomed 15(6):918–928

    Article  Google Scholar 

  32. Polesel A, Ramponi G, Mathews VJ (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9(3):505–510

    Article  Google Scholar 

  33. Trentacoste M, Mantiuk R, Heidrich W, Dufrot F (2012) Unsharp masking, countershading and halos: enhancements or artefacts? Comput Graph Forum 31(2):555–564

    Article  Google Scholar 

  34. Unsharp masking, Documentation, https://in.mathworks.com/help/images/ref/imsharpen.html

  35. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612

    Article  Google Scholar 

  36. Xiao L, Li C, Wu Z, Wang T (2016) An enhancement method for X-ray image via fuzzy noise removal and homomorphic filtering. Neurocomputing 195:56–64

    Article  Google Scholar 

  37. Zhao Y, Guo S, Luo M, Liu Y, Bilello M, Li C (2017) An energy minimization method for MS lesion segmentation from T1-w and FLAIR images. Magn Reson Imaging 39:1–6

    Article  Google Scholar 

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Correspondence to Justin Joseph.

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Joseph, J., Anoop, B.N. & Williams, J. A modified unsharp masking with adaptive threshold and objectively defined amount based on saturation constraints. Multimed Tools Appl 78, 11073–11089 (2019). https://doi.org/10.1007/s11042-018-6682-1

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