Image De-noising Method based on Wavelet Function Learning for Medical Image

SANGHUN, YUN and WON-SEOK, KANG (2015) Image De-noising Method based on Wavelet Function Learning for Medical Image. In: Second International Conference on Advances in Information Processing and Communication Technology - IPCT 2015, 18 - 19 April, 2015, Rome, Italy.

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Abstract

Medical imaging is playing the key role in diagnosing and treatment of diseases. For making accurate decisions, the images acquired by various medical imaging modalities must be free from noise. So image de-noising became an important pre-processing step in Medical image analysis. In this paper, we propose a new de-noising method for medical images. Our method divides up the medical image into multiwindows and assigns the optimal mother wavelet function to each windows. And we are using an n-gram based wavelet learning technique in order to investigate optimal wavelet sequences for an image de-noising. The wavelet learning approach uses Mean Square Error (MSE) as a feature to generate an n-gram table. The performance of the proposed method is compared with the existing methods using Peak Signal to Noise Ratio (PSNR). The results showed that the proposed method has a better PSNR than the previous methods

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Medical Image, De-noising, Wavelet Function Learning, N-gram
Depositing User: Mr. John Steve
Date Deposited: 02 May 2019 07:49
Last Modified: 02 May 2019 07:49
URI: http://publications.theired.org/id/eprint/1842

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