Computer-Aided Diagnosis for the Classification of Breast Masses in Automated Whole Breast Ultrasound Images
Journal
Ultrasound in Medicine and Biology
Journal Volume
37
Journal Issue
4
Pages
539-548
Date Issued
2011
Author(s)
Abstract
New automated whole breast ultrasound (ABUS) machines have recently been developed and the ultrasound (US) volume dataset of the whole breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student's t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features (p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of breast tumors. (E-mail: rfchang@csie.ntu.edu.tw). ? 2011 World Federation for Ultrasound in Medicine & Biology.
Subjects
Automated whole breast ultrasound; Breast cancer; Computer-aided diagnosis; Ellipsoid fitting; Logistic regression model
SDGs
Other Subjects
Area under the ROC curve; Benign and malignant tumors; Breast cancer; Breast mass; Breast tumor; Breast ultrasound; Computer-aided diagnosis system; Data sets; Ellipsoid fitting; Feature extraction and classification; Level sets; Logistic regression model; Logistic regression models; Mann-Whitney U test; Receiver operating characteristic curve analysis; ROC curves; Segmentation methods; Shape features; Statistical analysis; T-tests; Texture features; Automation; Curve fitting; Diseases; Feature extraction; Regression analysis; Textures; Three dimensional; Tumors; Ultrasonics; Computer aided diagnosis; adult; aged; article; automation; benign tumor; breast tumor; computer assisted diagnosis; controlled study; diagnostic accuracy; echography; female; human; image processing; image reconstruction; imaging system; male; malignant neoplastic disease; predictive value; priority journal; receiver operating characteristic; sensitivity and specificity; three dimensional imaging; tumor classification; tumor diagnosis; tumor volume; Adult; Aged; Algorithms; Artificial Intelligence; Breast Neoplasms; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Middle Aged; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Ultrasonography, Mammary; Young Adult
Type
journal article