Abstract
The quality of the medical image plays a major role in decision making by the radiologists. There exists a visual differentiation between the normal scene color images and medical images. Due to the low illumination and unavailability of the color parameter, medical images require more attention by radiologists for decision making. In this paper a new approach is proposed that enhances the quality of the Magnetic Resonance (MR) images. Proposed approach uses the spectral information present in form of Amplitude and Frequency within the MR image slices for an enhancement. The extracted enhanced spectral information gives better visualization as compared with original signal image generated from MR scanner. The quantitative analysis of the proposed approach suggests that the new method is far better than the traditional state-of-art image enhancement methods.
Acknowledgment
The authors would like to thank all the individuals who provide their guidance in implementation of this work. Moreover, the author is highly appreciative towards Dr. Sunil Jakhar, Department of Radio Diagnosis, SMS Medical College Jaipur, Rajasthan, India for providing his precious time towards this work in providing data samples, validating the experimental results and other suggestions for improving this work.
Research Funding: The author states that there is no funding involved with this work.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Conflict of Interest: The author states that there exists no conflict of interest.
Informed Consent: Informed consent has been obtained from all individuals included in this study.
Ethical Approval: The research related to human use complied with all the relevant national regulations and institutional policies and was performed in accordance with the tenets of the Helsinki declaration, and has been approved by the authors’ institutional review board.
References
1. Pei, SC, Chiu, YM. Background adjustment and saturation enhancement in ancient Chinese paintings. Trans Image Process 2006; 15: 3230–4. https://doi.org/10.1109/tip.2006.877478.Search in Google Scholar PubMed
2. Yu, YH, Kwok, NM, Ha, QP. Color tracking for multiple robot control using a system-on-programmable-chip. Autom Const 2011; 20: 669–76. https://doi.org/10.1016/j.autcon.2011.04.013.Search in Google Scholar
3. Guo, W, Xia, X, Xiaofei, W. A remote sensing ship recognition method based on dynamic probability generative model. Expert Syst Appl 2014; 41: 6446–58. https://doi.org/10.1016/j.eswa.2014.03.033.Search in Google Scholar
4. Hong, L, Wan, Y, Jain, A. Fingerprint image enhancement: algorithm and performance evaluation. Trans Pattern Anal Mach Intel 1998; 20: 777–89. https://doi.org/10.1109/34.709565.Search in Google Scholar
5. Kwok, NM, Shi, HY, Ha, QP, Fang, G, Chen, SY, Jia, X. Simultaneous image color correction and enhancement using particle swarm optimization. Eng Appl Artif Intell 2013; 26: 2356–71. https://doi.org/10.1016/j.engappai.2013.07.023.Search in Google Scholar
6. Jiang, G, Wong, CY, Lin, SCF, Rahman, MA, Ren, TR, Kwok, N, et al. Image contrast enhancement with brightness preservation using an optimal gamma correction and weighted sum approach. Mod Opt 2015; 62: 536–47. https://doi.org/10.1080/09500340.2014.991358.Search in Google Scholar
7. Strickland, RN, Kim, CS, McDonnell, WF. Digital color image enhancement based on the saturation component. Opt Eng 1987; 26: 26–34. https://doi.org/10.1117/12.7974125.Search in Google Scholar
8. Thomas, B, Strickland, R, Rodriguez, J. Color image enhancement using spatially adaptive saturation feedback. Int Conf Img Proc 1997; 3: 30–3. https://doi.org/10.1109/icip.1997.631967.Search in Google Scholar
9. Ngaiming, K, Haiyan, S, Gu, F, Quang, H, Ying-Hao, Y, Tonghai, W, et al. Color image enhancement using correlated intensity and saturation adjustments. Mod Opt 2015; 62: 1037–47. https://doi.org/10.1080/09500340.2015.1051601.Search in Google Scholar
10. Zebin, S, Wenquan, F, Qi, Z, Lidong, H. Brightness preserving image enhancement based on a gradient and intensity histogram. Electr Imag 2015; 24: 24–35. https://doi.org/10.1117/1.jei.24.5.053006.Search in Google Scholar
11. Purushothaman, J, Kamiyama, M, Taguchi, A. Color image enhancement based on Hue differential histogram equalization. Int Sympos Intell Sig Proc Comm Sys (ISPACS) 2016:1–5. https://doi.org/10.1109/ispacs.2016.7824720.Search in Google Scholar
12. Sodanil, M, Intarat, C. A development of image enhancement for CCTV images. In: 5th International Conference on IT Convergence and Sector (ICITCS). IEEE, United States; 2015.10.1109/ICITCS.2015.7292914Search in Google Scholar
13. Tingting, J, Guoyu, W. An approach to underwater image enhancement based on image structural decomposition. Oce Univ Chin 2015; 14: 255–60. https://doi.org/10.1007/s11802-015-2426-2.Search in Google Scholar
14. Zhiwei, Y, Mingwei, W, Zhengbing, H, Wei, L. An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm. Comp Intell Neuro 2015:1–12. https://doi.org/10.1155/2015/825398.Search in Google Scholar PubMed PubMed Central
15. Jindal, K, Gupta, K, Jain, M, Maheshwari, M. Bio-medical image enhancement based on spatial domain technique. Int Conf Adv Eng Tech Res (ICAETR) 2014:1–5. https://doi.org/10.1109/icaetr.2014.7012932.Search in Google Scholar
16. Wei-Yen, H, Ching-Yao, C. Medical image enhancement using modified color histogram equalization. Med Biol Eng 2015; 35: 580–4. https://doi.org/10.1007/s40846-015-0078-8.Search in Google Scholar
17. Amira, S, Sourav, S, Nilanjan, D, Noreen, K, Wahiba, BA, Aboul, EH. Computed Tomography image enhancement using cuckoo search: a log transform based approach. Signal Info Process 2015; 6: 244–57. https://doi.org/10.4236/jsip.2015.63023.Search in Google Scholar
18. Richard, TR. Image enhancement of cancerous tissue in mammography images [Dissertation of Doctor of Philosophy in Computer Science]. Florida: Nova Southeastern University; 2015.Search in Google Scholar
19. Jing, JW, Zhen, HJ, Xi, ZQ, Jie, Y, Nikola, K. Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast. Imag Sys Tech 2015; 25: 7–14. https://doi.org/10.1002/ima.22115.Search in Google Scholar
20. Khatkar, K, Kumar, D. Biomedical image enhancement using wavelets. Proc Comp Sci 2015; 48: 513–7. https://doi.org/10.1016/j.procs.2015.04.128.Search in Google Scholar
21. Qinli, Z, Shuting, S, Xiaoyun, S, Qi, G. A novel method of medical image enhancement based on wavelet decomposition. Automat Contr Comput Sci 2017; 51: 263–9. https://doi.org/10.3103/s0146411617040113.Search in Google Scholar
22. Loizou, P, Murray, V, Pattichis, MS, Seimenis, I, Pantziaris, M, Pattichis, CS. Multiscale amplitude-modulation frequency-modulation (AM–FM) texture analysis of multiple sclerosis in brain MRI images. Trans Info Tech Biomed 2011; 15: 119–29. https://doi.org/10.1109/titb.2010.2091279.Search in Google Scholar
23. Murray, V, Rodriquez, P, Pattichis, M. Multi-scale AM-FM demodulation and reconstruction methods with improved accuracy. Trans Imag Process 2010; 19: 1138–52. https://doi.org/10.1109/tip.2010.2040446.Search in Google Scholar
24. Zhou, W, Bovik, AC, Sheikh, HR, Simoncelli, EP. Image quality assessment: from error visibility to structural similarity. Trans Image Proc 2004; 13: 600–12. https://doi.org/10.1109/tip.2003.819861.Search in Google Scholar PubMed
© 2020 Walter de Gruyter GmbH, Berlin/Boston
