ArticleAsian Pacific journal of cancer prevention : APJCP2020
Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means.
Article in Asian Pacific journal of cancer prevention : APJCP, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 11 citations in OpenAlex.
- Towards to Optimal Wavelet Denoising Scheme-A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing.Sensors (Basel, Switzerland) · 2020Article
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2 authors.
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Abstract
objectiveThe survival rates of breast cancer are increasing as screening and diagnosis improve. The removal of noise is revealed to be a significant step for automatic - computer aided detection (CAD) of microcalcification in digital mammography.
methodsIn this paper, a combined approach for eradicating impulse noise from digital mammograms is proposed. The process is achieved in two stages, detection of noise followed by filtering of noise. The detection of noise is carried out by using Modified Robust Outlyingness Ratio (mROR) trailed by an extended NL (Non-Local)-means filter for filtering mechanism.
resultsAccording to the value of mROR, all pixels in mammogram images are divided into four distinct groups. In each cluster, many decision rules are then applied for detecting the impulse noise. Filtering is done with NL-means filter by providing a reference mammogram image.
conclusionThe comparative analysis and evaluated results are compared with some existing filters which indicate that the proposed structure outperforms the analysed result of others.
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