Modified Multi-Wavelet Noise Filtering Algorithm for Mammographic Image Denoising Using Recurrent Neural Network

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Swapnil Tamrakar
Swapnil Tamrakar
σ
Abha Choubey
Abha Choubey
ρ
Siddhartha Choubey
Siddhartha Choubey
α Chhattisgarh Swami Vivekanand Technical University

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Modified Multi-Wavelet Noise Filtering Algorithm for Mammographic Image Denoising Using Recurrent Neural Network

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Modified Multi-Wavelet Noise Filtering Algorithm for Mammographic Image Denoising Using Recurrent Neural Network Banner

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Abstract

The digital mammographic images are affected by several types of noises which require filters to denoise the noise level. This will help the medical practitioner to enhance the image quality of the mammograms and helps them in giving accurate diagnosis. There are so many works on image denoising technique but there are not much which gives emphasis on the mammographic images. . In application point of view medical images are classified as Multispectral Image (used for satellite surveillance), RGB standard colour scheme Image or other digital versions of the film image i.e., in our case its mammographic image. For every image type it requires different approach for denoising because in each type of image, it contains different factors in it. In denoising the mammographic image , the filtering technique that is to be applied depend on its noises at each resolution level of the microns to make the micro-classification of the cancerous tissues to that of the bright water dense patches caused by the calcium salts in the mammary glands. Thus, any single algorithm cannot provide similar performance range for different types of noise because not every method is effective for the scenario of mammographic image denoising. In the given study we have shown a method for the mammographic image denoising which is having higher accuracy and the performance range is suited for denoising applications.raphic image denoising.

References

13 Cites in Article
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  2. N Pandey,Z Salcic,J Sivaswamy (2000). Fuzzy logic based microcalcification detection.
  3. R Strickland,Hee Il Hahn (1996). Wavelet transforms for detecting microcalcifications in mammograms.
  4. M Mini,T Thomas (2003). A Neural Network Method for Mammogram Analysis Based on Statistical Features.
  5. S Yu,L Guan (2000). A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films.
  6. Cristiane Ferreira,Dı́bio Borges (2003). Analysis of mammogram classification using a wavelet transform decomposition.
  7. S Sentelle,C Sentelle,M Sutton (2002). Multiresolution-Based Segmentation of Calcifications for the Early Detection of Breast Cancer.
  8. R Nakayama,Y Uchiyama,K Yamamoto,R Watanabe,K Namba (2006). Computer-Aided Diagnosis Scheme Using a Filter Bank for Detection of Microcalcification Clusters in Mammograms.
  9. Paul Bao,Lei Zhang (2003). Noise reduction for magnetic resonance images via adaptive multiscale products thresholding.
  10. Mohammad Sameti,Rabab Ward,Jacqueline Morgan-Parkes,Branko Palcic (2009). Image Feature Extraction in the Last Screening Mammograms Prior to Detection of Breast Cancer.
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  12. A Choubey,G Sinha,S Choubey (2011). A hybrid filtering technique in medical image denoising: Blending of neural network and fuzzy inference.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Swapnil Tamrakar. 2015. \u201cModified Multi-Wavelet Noise Filtering Algorithm for Mammographic Image Denoising Using Recurrent Neural Network\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 15 (GJCST Volume 15 Issue G1): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-G Classification: F.1.1, I.2
Version of record

v1.2

Issue date

June 18, 2015

Language
en
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The digital mammographic images are affected by several types of noises which require filters to denoise the noise level. This will help the medical practitioner to enhance the image quality of the mammograms and helps them in giving accurate diagnosis. There are so many works on image denoising technique but there are not much which gives emphasis on the mammographic images. . In application point of view medical images are classified as Multispectral Image (used for satellite surveillance), RGB standard colour scheme Image or other digital versions of the film image i.e., in our case its mammographic image. For every image type it requires different approach for denoising because in each type of image, it contains different factors in it. In denoising the mammographic image , the filtering technique that is to be applied depend on its noises at each resolution level of the microns to make the micro-classification of the cancerous tissues to that of the bright water dense patches caused by the calcium salts in the mammary glands. Thus, any single algorithm cannot provide similar performance range for different types of noise because not every method is effective for the scenario of mammographic image denoising. In the given study we have shown a method for the mammographic image denoising which is having higher accuracy and the performance range is suited for denoising applications.raphic image denoising.

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Modified Multi-Wavelet Noise Filtering Algorithm for Mammographic Image Denoising Using Recurrent Neural Network

Swapnil Tamrakar
Swapnil Tamrakar Chhattisgarh Swami Vivekanand Technical University
Abha Choubey
Abha Choubey
Siddhartha Choubey
Siddhartha Choubey

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