|Title:||Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors||Authors:||Huang Y.-H.
|Keywords:||3-D morphology; Co-occurrence matrix; Computer-aided diagnosis; Ellipsoid-fitting||Issue Date:||2013||Journal Volume:||112||Journal Issue:||3||Start page/Pages:||508-517||Source:||Computer Methods and Programs in Biomedicine||Abstract:||
This study aimed to evaluate the value of using 3-D breast MRI morphologic features to differentiate benign and malignant breast lesions. The 3-D morphological features extracted from breast MRI were used to analyze the malignant likelihood of tumor from ninety-five solid breast masses (44 benign and 51 malignant) of 82 patients. Each mass-like lesion was examined with regards to three categories of morphologic features, including texture-based gray-level co-occurrence matrix (GLCM) feature, shape, and ellipsoid fitting features. For obtaining a robust combination of features from different categories, the biserial correlation coefficient (|rpb|)≧0.4 was used as the feature selection criterion. Receiver operating characteristic (ROC) curve was used to evaluate performance and Student's t-test to verify the classification accuracy. The combination of the selected 3-D morphological features, including conventional compactness, radius, spiculation, surface ratio, volume covering ratio, number of inside angular regions, sum of number of inside and outside angular regions, showed an accuracy of 88.42% (84/95), sensitivity of 88.24% (45/51), and specificity of 88.64% (39/44), respectively. The AZ value was 0.8926 for these seven combined morphological features. In conclusion, 3-D MR morphological features specified by GLCM, tumor shape and ellipsoid fitting were useful for differentiating benign and malignant breast masses. ? 2013 Elsevier Ireland Ltd.
|ISSN:||0169-2607||DOI:||10.1016/j.cmpb.2013.08.016||SDG/Keyword:||Benign and malignant tumors; Classification accuracy; Co-occurrence-matrix; Differential analysis; Ellipsoid-fitting; Gray level co-occurrence matrix; Morphological features; Receiver operating characteristic curves; Computer aided diagnosis; Tumors; Morphology; adult; aged; article; breast cancer; breast tumor; computer assisted diagnosis; diagnostic accuracy; diagnostic test accuracy study; female; human; image analysis; major clinical study; morphology; nuclear magnetic resonance imaging; nuclear magnetic resonance scanner; sensitivity and specificity; three dimensional imaging; tumor differentiation; tumor volume; 3-D morphology; Co-occurrence matrix; Computer-aided diagnosis; Ellipsoid-fitting; Breast Neoplasms; Diagnosis, Computer-Assisted; Diagnosis, Differential; Female; Humans; Magnetic Resonance Imaging
|Appears in Collections:||醫學系|
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