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IJSTR >> Volume 9 - Issue 2, February 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Denoising Of Osteosarcoma Digital Images Using Various Enhanced Filtering Techniques

[Full Text]

 

AUTHOR(S)

B.Karthicsonia, Dr. M.Vanitha

 

KEYWORDS

Osteosarcoma, Image processing Noise, Filtering, Denoising parameters.

 

ABSTRACT

Medical image processing is a dominant procedure that is used to predict different variety cancers such as bone cancer, brain cancer, lung cancer, liver cancer, breast cancer, etc. Osteosarcoma is a one of the category of bone cancer. It is the most horrible cancer that affects human being’s bones. It can be predicted with the help Computed Tomography scan images, Magnetic resonance imaging (MRI) Scan image, Bone scan, X-rays and Histopathological tissue images. These digital medical images plays vital role in diagnostic a patient. The most important feature of investigative medical image is to reduce or compress the Noise. Noises are removed by various filtering techniques. This paper compares performance of a various filter techniques used in removing different types of noises in medical images. The image quality is measured by denoising parameters such as MSE, RMSE and PSNR. MATLAB is used as a programming tool.

 

REFERENCES

[1] Patidar, Pawan, "Image de-noising by various filters for different noise." IJCA (2010)
[2] Basu, Mitra. "Gaussian-based edge-detection methods-a survey." IEEE Transactions on Systems, Man, and Cybernetics.
[3] Hamad,"De-noising of medical images by using some filters." IJBResearch(2014).
[4] Wang, "Digital image enhancement: a survey." Computer Vision, Graphics, and Image Processing (1983)
[5] Kumar, "Comparative performance analysis of image de-noisingtechniques." ArXiv. (2019).
[6] H. Choi "Analysis of wavelet domain Wiener filters," IEEE Int. Symp. Time- Frequency and Time-Scale Analysis, Oct. 1998.
[7] H. Zhang, “Image denoising via wavelet-domain spatially adaptive FIR Wiener filtering”, IEEE Proc. Int. Conf. Acoustic., Speech, Signal Processing, June 2000.
[8] R. G. Baraniuk, “Optimal tree approximation with wavelets,” in Proc. SPIE Tech. Conf. Wavelet Applications Signal Processing ,1999.
[9] M. Lang,, "Nonlinear processing of a shift invariant DWT for noise reduction," SPIE, Mathematical Imaging: Wavelet Applications for Dual Use, April 1995.
[10] KAM SajadHyder. And Vanitha M “A Survey On various Image Retrieval Techniques (IJARTET) Vol.3 (20), pp.623-626 ISSN: 2394-3785.