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A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection
- Source :
- Computational and Mathematical Methods in Medicine, Vol 2015 (2015), Computational and Mathematical Methods in Medicine
- Publication Year :
- 2015
- Publisher :
- Hindawi Limited, 2015.
-
Abstract
- This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.
- Subjects :
- Adult
Support Vector Machine
Article Subject
Computer science
Contrast Media
Scale-space segmentation
Breast Neoplasms
Image processing
Feature selection
lcsh:Computer applications to medicine. Medical informatics
Sensitivity and Specificity
General Biochemistry, Genetics and Molecular Biology
Pattern Recognition, Automated
Resampling
Image Processing, Computer-Assisted
Humans
Preprocessor
Computer vision
Segmentation
Breast
Diagnosis, Computer-Assisted
Aged
General Immunology and Microbiology
business.industry
Air
Applied Mathematics
Reproducibility of Results
General Medicine
Middle Aged
Magnetic Resonance Imaging
Support vector machine
Computer-aided diagnosis
Modeling and Simulation
lcsh:R858-859.7
Female
Artificial intelligence
business
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 17486718
- Volume :
- 2015
- Database :
- OpenAIRE
- Journal :
- Computational and Mathematical Methods in Medicine
- Accession number :
- edsair.doi.dedup.....36534339c423cf27d589e953cc83bfc0