CT scan contrast enhancement using singular value decomposition and adaptive gamma correction
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We propose in this paper a new enhancement algorithm dedicated to dark computed tomography (CT) scan based on discrete wavelet transform with singular value decomposition (DWT–SVD) followed by adaptive gamma correction (AGC). Discrete wavelet transform (DWT) is considered to decompose the input dark CT image in four sub-bands. Singular value decomposition (SVD) is used in order to compute the corresponding singular value matrix of low–low (LL) sub-band image. The enhanced LL sub-band is determined by scaling the singular value matrix of original LL sub-band by an adequate correction factor, followed by inverse SVD. For a further contrast improvement, the new enhanced LL sub-band image is processed using an AGC algorithm. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed sub-bands to generate the final enhanced image. This proposed method has the advantage of being fully automatic and could be applied for dark input images with either low or moderate contrast. Different dark CT images are considered to compare the performance of our proposed method to three other enhancement techniques using both objective and subjective assessments. Simulation results show that our proposed algorithm consistently produces good contrast enhancement, with best brightness and edges details conservation and with minimum added distortions to the enhanced CT images.
KeywordsDark CT scan Contrast enhancement SVD AGC
The author would like to thank the Deanship of Scientific Research at Majmaah University for funding this work under Project No. 37/109.
- 2.Al-Ameen, Z., Al-Ameen, S., Sulong, G.: Latest methods of image enhancement and restoration for computed tomography: a concise review. Appl. Med. Inform. 36(1), 1–12 (2015)Google Scholar
- 13.Tiwari, M., Gupta, B.: Brightness preserving contrast enhancement of medical images using adaptive gamma correction and homomorphic filtering. In: IEEE Students’ Conference on Electrical, Electronics and Computer Science (2016)Google Scholar
- 15.Al-Juboori, R.A.: Contrast enhancement of the mammographic image using retinex with clahe methods. Iraqi J. Sci. 58(1B), 327–336 (2017)Google Scholar
- 16.Ganesan, B., Yamuna, G., Suman, S.K.: Hybrid contrast enhancement approach for medical image. In: International Journal of Computer Applications, Proceedings on National Conference on Emerging Trends in Information Communication Technology (2013)Google Scholar
- 18.Kaur, N., Singh, E.: Enhancement of medical images using histogram based hybrid technique. Int. J. Adv. Eng. Manag. Sci. 2(9), 1425–1432 (2016)Google Scholar
- 21.Demirel, H., Anbarjafari, G., Jahromi, M.N.: Image equalization based on singularvalue decomposition. In: 23rd IEEE International Symposium on Computer and. Information Sciences, pp. 1–5 (2008)Google Scholar
- 23.Bhandari, A.K., Kumar, A., Padhy, P.K.: Enhancement of low contrast satelliteimages using discrete cosine transform and singular value decomposition. World Acad. Sci. Eng. Technol. 55, 35–41 (2011)Google Scholar
- 25.Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall, Pearson, Upper Saddle River (2008)Google Scholar
- 27.Frosio, I.: Real time enhancement of cephalometric radiographies. In: 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, number 3, pp. 972–975 (2006)Google Scholar
- 29.Somasundaram, K., Kalavathi, P.: Medical image contrast enhancement based on gamma correction. Int. J. Knowl. Manag. e-Learn. 3(1), 15–18 (2011)Google Scholar
- 30.Rahman, S., Rahman, M.M., Al Wadud, M.A., Al Quaderi, G.D., Shoyaib, M.: An adaptive gamma correction for image enhancement. EURASIP J. IVP 35, 2–13 (2016)Google Scholar