ArticleJournal of medical systems2016
Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Facet joint distance measurement using digital tomosynthesis while standing.Journal of biomechanics · 2025Article
- Automated Detection and Classification of Microcalcification Clusters with Enhanced Preprocessing and Fractal AnalysisAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology.Journal of healthcare engineering · 2018Article
- Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.Journal of medical systems · 2017Article
- A Multi Directional Perfect Reconstruction Filter Bank Designed with 2-D Eigenfilter Approach: Application to Ultrasound Speckle Reduction.Journal of medical systems · 2017Article
- A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis.Journal of healthcare engineering · 2017Article
- A Novel Solution Based on Scale Invariant Feature Transform Descriptors and Deep Learning for the Detection of Suspicious Regions in Mammogram Images.Journal of medical signals and sensorsArticle
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Authors and funding
2 authors.
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Abstract
Detection of mass in mammogram for early diagnosis of breast cancer is a significant assignment in the reduction of the mortality rate. However, in some cases, screening of mass is difficult task for radiologist, due to variation in contrast, fuzzy edges and noisy mammograms. Masses and micro-calcifications are the distinctive signs for diagnosis of breast cancer. This paper presents, a method for mass enhancement using piecewise linear operator in combination with wavelet processing from mammographic images. The method includes, artifact suppression and pectoral muscle removal based on morphological operations. Finally, mass segmentation for detection using adaptive threshold technique is carried out to separate the mass from background. The proposed method has been tested on 130 (45 + 85) images with 90.9 and 91 % True Positive Fraction (TPF) at 2.35 and 2.1 average False Positive Per Image(FP/I) from two different databases, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). The obtained results show that, the proposed technique gives improved diagnosis in the early breast cancer detection.
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