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Tropical Journal of Pharmaceutical Research
Pharmacotherapy Group, Faculty of Pharmacy, University of Benin, Benin City, Nigeria
ISSN: 1596-5996
EISSN: 1596-9827
Vol. 15, No. 9, 2016, pp. 1983-1993
Bioline Code: pr16262
Full paper language: English
Document type: Research Article
Document available free of charge

Tropical Journal of Pharmaceutical Research, Vol. 15, No. 9, 2016, pp. 1983-1993

 en Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images
Dogra, Ayush; Agrawal, Sunil & Goyal, Bhawna

Abstract

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures.
Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations.
Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images.
Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.

Keywords
Ripplet; Directional discrete cosine transform (DDCT); Peak signal to noise ratio; MSE (mean square error); SC (structural content); MD (maximum difference); NCC (normalized cross correlation

 
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