Wavelet Threshold DeNoising for Mammogram Images

Digital mammograms are coupled with noise which makes de-noising a challenging problem. In the literature, few wavelets like daubechies db3 and haar have been used for de-noising medical images. However, wavelet filters such as sym8, daubechies db4 and coif1 at certain level of soft and hard threshold have not been taken into account for mammogram images. Therefore, in this study five wavelet filters namely: haar, sym8, daubechies db3, db4 and coif1 at certain level of soft and hard threshold have been considered. Later, peak signal to noise ratio and mean squared error values are calculated. From the obtained results, it can be concluded that db3 (46.44656 db for hard threshold and 43.80779 db for soft threshold) is more appropriate filter for de-noising mammogram images while compared with other wavelets filters.

[1]  Vittoria Bruni,et al.  Combined image compression and denoising using wavelets , 2007, Signal Process. Image Commun..

[2]  Mohammad Reza Zare,et al.  Automatic Classification Of Medical X-Ray Images , 2013, Malaysian Journal of Computer Science.

[3]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[4]  Rosziati Ibrahim,et al.  Performance Comparison of Selected Classification Algorithms Based on Fuzzy Soft Set for Medical Data , 2015 .

[5]  Mohammed Gulam Ahamad,et al.  Denoising of Medical Images Using Multiwavelet Transforms and Various Thresholding Techniques , 2013 .

[6]  Tae-Sun Choi,et al.  Quantum and impulse noise filtering from breast mammogram images , 2012, Comput. Methods Programs Biomed..

[7]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[8]  Ishpreet Singh Virk,et al.  Medical Image Denoising In The Wavelet Domain Using Haar And DB3 Filtering , 2012 .

[9]  Fei Peng,et al.  Pre-Processing of CT Brain Images for Content-Based Image Retrieval , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[10]  Jing Shen,et al.  Segmentation of Ultrasound Image Based on Texture Feature and Graph Cut , 2008, CSSE.

[11]  N. Suthanthira Vanitha,et al.  The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images , 2013 .

[12]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[13]  Seong-Hoon Kim,et al.  X-ray image classification using Random Forests with Local Binary Patterns , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[14]  Ying Zheng,et al.  Analysis of signal de-noising method based on an improved wavelet thresholding , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[15]  I. Daubechies.  Ten Lectures on Wavelets , 1992 .

[16]  Rosziati Ibrahim,et al.  Enhancement of Medical Image Compression by Using Threshold Predicting Wavelet-Based Algorithm , 2015 .

[17]  Rosziati Ibrahim,et al.  A Framework for Medical Images Classification Using Soft Set , 2013 .