ArticleJournal of digital imaging2015
An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.
Article in Journal of digital imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Psychometric Properties of the Chinese Version of the Gilliam Autism Rating Scale-Third Edition (CV-GARS-3).Journal of autism and developmental disorders · 2026Article
- Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images.PloS one · 2024Article
- ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images.PloS one · 2024Article
- Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images.Scientific reports · 2023Article
- Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.Cancers · 2022Review
- Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation.Diagnostics (Basel, Switzerland) · 2022Article
- Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor.Visual computing for industry, biomedicine, and art · 2022Article
- Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm.BioMed research international · 2022Article
- A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine.Physical and engineering sciences in medicine · 2021Article
- Breast Cancer Segmentation Methods: Current Status and Future Potentials.BioMed research international · 2021Review
- A complementary scheme for automated detection of high-uptake regions on dedicated breast PET and whole-body PET/CT.Radiological physics and technology · 2019Article
- A new approach for detecting abnormalities in mammograms using a computer-aided windowing system based on Otsu's method.Radiological physics and technology · 2019Article
- Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction.In silico pharmacology · 2018Article
- Breast masses in mammography classification with local contour features.Biomedical engineering online · 2017Article
- Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer.Journal of digital imaging · 2017Article
- Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.Computational and mathematical methods in medicine · 2017Review
- Applying Data Mining Techniques to Improve Breast Cancer Diagnosis.Journal of medical systems · 2016Article
- 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73%, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.
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