Denoising of Mammographic Images from Quantum Noise in Wavelet Domain
Smriti Bhatnagar1, Richa Gupta2
1Smriti Bhatnagar, Department of Engineering and Communication Engineering, Japee Institute of Information Technology, NOIDA,
2Richa Gupta, Department of Engineering and Communication Engineering, Japee Institute of Information Technology, NOIDA,
Manuscript received on 15 April 2019 | Revised Manuscript received on 19 May 2019 | Manuscript published on 30 May 2019 | PP: 435-440 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3426058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Telemedicine is the new trends in Health care services. Advances in Electronics, Imaging Techniques and Communication Engineering help a lot in early diagnosis and fast recovery of some fatal diseases. As an example case of breast cancer is considered here. Digital Mammographic images are used for diagnosis of Breast cancer. But they are contaminated by quantum noise during acquisition due to the nature of low energy photons used for Mammographic imaging modality. To remove the Quantum Noise in mammographic images, use of Discrete Wavelet Transforms (DWT) is gaining momentum due to unique properties of sparcity and multiresolution and easy implementation of DWTs with different Digital Filters. Thresholding techniques such as Visu Shrink, Bayes Shrink, Neigh Shrink and Modified Neighborhood are used in this paper and three different wavelets as Haar, Db4 and Sym4 has been used. The Performance Metrics such as Peak Signal to Noise Ratio (PSNR), Mean square Error (MSE), Structural Similarity Index (SSIM) and Edge Preserving Index (EPI) are used to evaluate the performance of Denoising algorithms.
Index Terms: Tele Mammograph; Discrete Wavelet Transform (DWT); Denoising; Thresholding.
Scope of the Article: Nano Electronics and Quantum Computing