Abstract
Nowadays the skin cancer has become a more dangerous and an unpredictable disease among the humans. Nearly one million of people all over the world every year are been affected by the skin cancer and left with no treatment due to the lack of early diagnosis. Besides the usual types of cancer such as the melanoma, basal cell carcinoma and squamous cell carcinoma that could be identified easily there are certain types of unusual skin cancer such as the Merkel cell skin cancer that are rare and difficult to diagnose. As the identification of the Merkel cell skin cancer at the early stage would be very useful in deciding the necessary treatment for its cure, the paper has put forward preprocessing techniques to improve the image quality to make the further image processing procedure easy in the identification of the skin cancer. The proposed method applies the combined image enhancement and the restoration (CIEIR) on the input skin lesion images and makes it more presentable with the improved quality for the further image processing steps in the identification of the normal skin and the skin affected by the Merkel cell tumor. The CIEIR is implemented in the MATLAB and the parameters such as the PSNR, SSIM and the MSE are measured.
References
- https://www.slideshare.net/abhiabhinay/image-enhancement-techniques-24165028
- https://www.slideshare.net/kalyanacharjya/image-restoration-40589017
- Sezn, M. I., A. M. Teklap, and Ralph Schaetzing. "Automatic anatomically selective image enhancement in digital chest radiography." IEEE Transactions on Medical Imaging 8, no. 2 (1989): 154-162.
- Shi, Zhixin, and Venu Govindaraju. "Character image enhancement by selective region-growing." Pattern recognition letters 17, no. 5 (1996): 523-527.
- Lagendijk, Reginald L., Jan Biemond, and Dick E. Boekee. "Regularized iterative image restoration with ringing reduction." IEEE Transactions on acoustics, speech, and signal processing 36, no. 12 (1988): 1874-1888.
- Figueiredo, Mário AT, and Robert D. Nowak. "An EM algorithm for wavelet-based image restoration." IEEE Transactions on Image Processing 12, no. 8 (2003): 906-916.
- Katsaggelos, Aggelos K. Digital image restoration. Springer Publishing Company, Incorporated, 2012.
- Irmak, Emrah, and Ahmet H. Ertas. "A review of robust image enhancement algorithms and their applications." In 2016 IEEE Smart Energy Grid Engineering (SEGE), pp. 371-375. IEEE, 2016.
- Zhao, Hang, Orazio Gallo, Iuri Frosio, and Jan Kautz. "Loss functions for image restoration with neural networks." IEEE Transactions on Computational Imaging 3, no. 1 (2016): 47-57.
- Joshi, Shilpa, and R. K. Kulkarni. "Medical Image Enhancement Using Hybrid Techniques for Accurate Anomaly Detection And Malignancy Predication." In Third International Congress on Information and Communication Technology, pp. 951-961. Springer, Singapore, 2019.
- Hu, Kai, Si Liu, Yuan Zhang, Chunhong Cao, Fen Xiao, Wei Huang, and Xieping Gao. "Automatic segmentation of dermoscopy images using saliency combined with adaptive thresholding based on wavelet transform." Multimedia Tools and Applications (2019): 1-18.
- Singh, Monica, Sujala Pradhan, Md Ruhul Islam, and N. Chitrapriya. "A Comparative Study on Different Genres of Image Restoration Techniques." In Advances in Communication, Devices and Networking, pp. 373-383. Springer, Singapore, 2019.
- Kumar, Rahul, Brajesh Kumar Kaushik, and R. Balasubramanian. "Blur and noisy image restoration for near real time applications." In Applications of Digital Image Processing XLII, vol. 11137, p. 111370W. International Society for Optics and Photonics, 2019.
- Ali, Sahlah Abed. "Image Enhancement Techniques for Images at Blur Motion and Different Noises." AL-Rafidain Journal of Computer Sciences and Mathematics 13, no. 1 (2019): 48-60.
- Sun, Sashuang, Qian Wu, Lin Jiao, Yan Long, Dongjian He, and Huaibo Song. "Recognition of green apples based on fuzzy set theory and manifold ranking algorithm." Optik 165 (2018): 395-407.