Abstract
Dental X-ray imaging is one of the medical imaging techniques used by the dental practitioners. This method uses dental X-ray diagnostic file. This has been found to be very helpful to dental doctors to get more information on the oral disease in patients. X-ray radiation is used for wide range of medical imaging applications. Once the technique becomes a success, it produces good quality X-ray images. The X-ray image is very essential for the treatment planning and procedure for the patients. The X-ray image has lot of artifacts which can be removed through various preprocessing methods. In this paper a novel method for preprocessing is introduced through the fusion of adaptive histogram, morphological enhancement and wavelet de-noising (AWHM). The input X-ray image is initially checked with various other existing preprocessing methods such as adaptive histogram equalization, un sharp masking, Gaussian low pass and high pass methods, high pass adaptation, morphological enhancement, contrast enhancement and wavelet de-noising. AWHM is giving better result than all the other methods. The existing method and novel method is compared with various non-reference parameters and the reference parameter. The result of the novel method is better than all other existing methods. The Contrast Per Pixel technique is used for analyzing the pixel brightness in a more better way. Since the CPP value is higher in AMHW method it indicates AMHW method is the best among the pre-existing methods.
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References
Yaroslavsky, L.P.: Local adaptive image restoration and enhancement with the use of DFT and DCT in a running window. In: Proceedings of SPIE, Bellingham, vol. 2825, pp. 2–13 (1996)
Mehdizadeh, M., Dolatyar, S.: Study of effect of adaptive histogram equalization on image quality in digital preapical image in pre apex area. Res. J. Biol. Sci. 4(8), 922–924 (2009)
Regezi, J.A.: Periapical disease: spectrum and differentiating features. J. Calif. Dent. Assoc. 27, 285–289 (1999)
Langland, O.E., Langlais, R.P. Preece, J.W.: Principles of Dental Imaging. Lippincott Williams & Wilkins (2002)
Noor, M., Khalid, N.E.A., Ali, M.H., Numpang, A.D.A.: Enhancement of soft tissue lateral neck radiograph with fish bone impaction using adaptive histogram equalization (AHE). In: The 2nd International Conference on Computer Research and Development (2010)
Khalid, N.E.A., Manaf, M., Aziz, M.E., Ali, M.H.:CR images of metacarpal cortical edge detection-bone profile histogram approximation method. In: Intelligent and Advanced Systems, 2007, ICIAS 2007, pp. 702–708, 25–28 Nov 2007
Sakata, M., Ogawapp, K.: Noise reduction and contrast enhancement for small-dose x-ray images in wavelet domain. In: Presented at the Nuclear Science Symposium Conference record (NSS/MIC) (2009)
Aufrichtig, R., Xue, P.L.: Dose efficiency and low-contrast detectability of an amorphous silicon X-ray detector for digital radiography. Phys. Med. Biol. 45, 2653–2669 (2000)
Tiwari, R.B.: Dental X-ray image enhancement based on human visual system and local Image statistic. In: Proceeding of the International Conference of Image Processing, Computer Vision and Pattern Recognition, vol. I&II, pp. 100–106 (2006)
Gonzales, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)
Thangavel, K., Manavalan, R., Aroquiaraj, I.L.: Removal of speckle noise from ultrasound medical image based on special filters: comparative study. ICGST-GVIP J. 9(III), 25–32 (2009)
Pisano, E.D.: Contrast limits adaptive histogram equalization image processing to improve the detection of simulated speculations in dense mammograms. J. Digital Imag. 11, 193–200 (1998)
Mahmoud, T.A., Marshall, S.: Medical image enhancement using threshold decomposition driven adaptive morphological filter. In: Presented at the 16th European Signal Processing Conference (EUSIPCO), Laussane, Swizerland (2008)
Foi, A.: Noise estimation and removal in MR imaging: the variance-stabilization approach. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1809–1814 (2011)
Grundland, M., Dodgson, N.A.: Decolorize: fast, contrast enhancing, color to gray scale on version. Pattern Recogn. 40(11), 891–2896 (2007)
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Simon, S.G.S., Joseph, X.F., Waktola, A.T., Senay, D. (2019). A Novel Method of Pre-processing Using Dental X-Ray Images by Adaptive Morpho Histo Wavelet Denoising (AMHW) Method. In: Mekuria, F., Nigussie, E., Tegegne, T. (eds) Information and Communication Technology for Development for Africa. ICT4DA 2019. Communications in Computer and Information Science, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-26630-1_1
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DOI: https://doi.org/10.1007/978-3-030-26630-1_1
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