Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors

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• 作者：Yan-Hao Huang ; Yeun-Chung Chang ; Chiun-Sheng Huang ; Tsung-Ju Wu ; Jeon-Hor Chen ; Ruey-Feng Chang
• 关键词：3-D morphology ; Co-occurrence matrix ; Ellipsoid-fitting ; Computer-aided diagnosis
• 刊名：Computer Methods and Programs in Biomedicine
• 出版年：2013
• 出版时间：December, 2013
• 年：2013
• 卷：112
• 期：3
• 页码：508-517
• 全文大小：1580 K

文摘

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 (|r_pb|) ¨R 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 A_Z 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.