Design and implementation of non-perfect reconstruction biorthogonal wavelets for edge detection of X-ray images

P.M.K. Prasad, G. Sasibhushana Rao, M.N.V.S.S. Kumar and K. Chiranjeevi

International Journal of Data Science, 2018, vol. 3, issue 1, 50-67

Abstract: The X-ray bone images are extensively used by the medical practitioners to detect the minute fractures as they are painless and economical compared with other imaging modalities. Edge detection of X-ray bone image is very useful for the medical practitioners as it provides important information for diagnosis which, in turn, enables them to give better treatment decisions to the patients. This paper proposes design and implementation of non-perfect reconstruction biorthogonal wavelet for the edge detection of X-ray images. The non-perfect reconstruction biorthogonal wavelet NPR Zbo6.5 wavelet performs well in detecting the edges with better quality. The simulation results show that the non-prefect reconstruction biorthogonal wavelet is effective and accurate. The non-perfect reconstruction biorthogonal wavelet is superior to perfect reconstruction (PR) biorthogonal wavelet for edge detection of X-ray images. The various performance metrics like ratio of edge pixels to size of an image (REPS), peak signal to noise ratio (PSNR) and computation time are compared for various biorthogonal wavelets.

Keywords: filter-banks; non-perfect reconstruction; symmetry; biorthogonal; edge detection; threshold; edge points; peak signal to noise ratio; support interval; vanishing moments. (search for similar items in EconPapers)
Date: 2018
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