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Reseach Article

An Enhanced Approach for Preprocessing of Mammogram Images using Inverse Daubechies Wavelet Transform and Non-Linear Diffusion

by S. Kowsalya, D. Shanmuga Priyaa
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 5
Year of Publication: 2015
Authors: S. Kowsalya, D. Shanmuga Priyaa
10.5120/ijca2015906955

S. Kowsalya, D. Shanmuga Priyaa . An Enhanced Approach for Preprocessing of Mammogram Images using Inverse Daubechies Wavelet Transform and Non-Linear Diffusion. International Journal of Computer Applications. 130, 5 ( November 2015), 6-12. DOI=10.5120/ijca2015906955

@article{ 10.5120/ijca2015906955,
author = { S. Kowsalya, D. Shanmuga Priyaa },
title = { An Enhanced Approach for Preprocessing of Mammogram Images using Inverse Daubechies Wavelet Transform and Non-Linear Diffusion },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 5 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number5/23203-2015906955/ },
doi = { 10.5120/ijca2015906955 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:24:31.966923+05:30
%A S. Kowsalya
%A D. Shanmuga Priyaa
%T An Enhanced Approach for Preprocessing of Mammogram Images using Inverse Daubechies Wavelet Transform and Non-Linear Diffusion
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 5
%P 6-12
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is the most leading cause of death in women nowadays. Screening mammography is currently the best available radiological technique for early detection of breast cancer. The detection of breast cancer is disturbed due to the existence of artifacts which reduce the rate of accuracy. For this reason, the pre-processing of mammogram images is very important in the process of breast cancer analysis because it reduces the number of false positives. This paper discusses about two existing filtering techniques and compares it with the results of a proposed filtering method. It is used to solve the noise removal problems and separate the background region from the breast profile region using an automatic thresholding technique. The results are evaluated on the pre-processing method on a set of images obtained from MIAS database. Thus this preparation phase improves the image quality and accentuates the CAD results more accurately.

References
  1. Kother Mohideen, Arumuga Perumal, Krishnan, Mohamed Sathik. 2011. Image denoising and enhancement using Multiwavelet with Hard Threshold in Digital Mammographic images, International Arab Journal of e-Technology, Vol. 2, No. 1.
  2. Caroline Arboleda, Zhantian Wang and Macro Stampanoni. 2013. Wavelet based noise-model driven denoising algorithm for differential phase contrast mammography, Optical society of America, Vol. 21, No. 9.
  3. Tarun Kumar Agarwal, Mayank Tiwari, Subir Singh Lamba. 2014. Modified histogram based contrast enhancement using homomorphic filtering for medical images, Advance Computing Conference (IACC), 2014 IEEE International.
  4. Sharma, J, Rai, J.K. ; Tewari, R.P. 2014. Identification of pre-processing technique for enhancement of mammogram images, Medical Imaging, m-Health and Emerging Communication Systems (MedCom), 2014 International Conference.
  5. P.Mayo, F.Rodenas, G. Verdu. Sep 2004. Comparing methods to denoise mammographic images, Proceedings of 25th Annula International Conference of IEEE EMBS.
  6. Taranjot Kaur. 2014. Image denoising Algorithms and DWT: A Review, Int. Journal of Computer science and Information Technologies Vol. 5 No. 5.
  7. Suman Shreshta. 2014. Image denoising using New adaptive Based Median Filter”, Int. Journal of Signal and image Processing, Vol. 5, No. 4.
  8. Anilet Bala, chiranjeeb Hati, CH Punith. 2014. Image denoising using Wavelet transform and wiener filter, Int. Journal of advanced Research in Electrical, electronics and Instrumentation Engineering”, Vol. 3 No.1 , Jan 2014.
  9. Juan Shan, Wen Ju, Yanhui, Guoa, Ling Zhang , H.D. Cheng. 2010. Automated breast cancer detection and classification using ultrasound images: A survey , Pattern Recognition 43 299 – 317.
  10. Indra Kanta Maitra, Sanjay Nag, Samir Kumar Bandyopadhyay. 2011. Technique for preprocessing of digital mammogram, computer methods and program and medicine.
  11. Seng Kah Phooi, Man Zhihong, H. R. Wu. 2002. A New Approach in Fuzzy Adaptive Filtering, Fuzzy Logic Studies in Fuzziness and Soft Computing Volume 81, pp 277-287.
  12. R.N. Strick. 1996. Wavelet transforms for detecting microcalcifications in mammograms, IEEE Trans. Med. Imaging Vol. 15 No 2 pp 218–229.
  13. S.N. Yu, K.Y. Li, Y.K. Huang. 2006. Detection of microcalcifications in digital mammograms using wavelet filter and Markov random field model, Comput. Med. Imaging Graphics 30 163-173.
Index Terms

Computer Science
Information Sciences

Keywords

Breast Cancer Detection Mammogram images pre-processing image de-noising filtering breast contour detection pectoral muscle extraction Inverse Daubechies wavelet transform non-linear diffusion.