ArticleJournal of digital imaging2017
Microcalcification Segmentation from Mammograms: A Morphological Approach.
Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models.Sensors (Basel, Switzerland) · 2024Article
- A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.Journal of cancer research and clinical oncology · 2023Article
- Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach.Journal of digital imaging · 2023Article
- A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation.Journal of digital imaging · 2022Article
- Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification.Computers in biology and medicine · 2022Article
- A new conditional region growing approach for microcalcification delineation in mammograms.Medical & biological engineering & computing · 2021Article
- Breast Mass Detection in Mammography Based on Image Template Matching and CNN.Sensors (Basel, Switzerland) · 2021Article
- Breast Cancer Segmentation Methods: Current Status and Future Potentials.BioMed research international · 2021Review
- A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms.Journal of digital imaging · 2019Article
- Detection of potential microcalcification clusters using multivendor for-presentation digital mammograms for short-term breast cancer risk estimation.Medical physics · 2019Review
- Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images.Journal of digital imaging · 2018Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512 × 512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones. The experiments carried out yielded the following average values of the measured indices: 80.5% (similarity index), 75.7% (overlap fraction), 70.8% (overlap value), and 19.8% (extra fraction). The average time of executing all steps of the methods used for a single ROI amounted to 0.83 s.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.