TY - JOUR
T1 - Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography
AU - Lo, Chung Ming
AU - Chang, Yeun Chung
AU - Yang, Ya-Wen
AU - Huang, Chiun Sheng
AU - Chang, Ruey Feng
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - Background: Elastography is a new sonographic imaging technique to acquire the strain information of tissues and transform the information into images. Radiologists have to observe the gray-scale distribution of tissues on the elastographic image interpreted as the reciprocal of Young[U+05F3]s modulus to evaluate the pathological changes such as scirrhous carcinoma. In this study, a computer-aided diagnosis (CAD) system was developed to extract quantitative strain features from elastographic images to reduce operator-dependence and provide an automatic procedure for breast mass classification. Method: The collected image database was composed of 45 malignant and 45 benign breast masses. For each case, tumor segmentation was performed on the B-mode image to obtain tumor contour which was then mapped to the elastographic images to define the corresponding tumor area. The gray-scale pixels around tumor area were classified into white, gray, and black by fuzzy c-means clustering to highlight stiff tissues with darker values. Quantitative strain features were then extracted from the black cluster and compared with the B-mode features in the classification of breast masses. Results: The performance of the proposed strain features achieved an accuracy of 80% (72/90), a sensitivity of 80% (36/45), a specificity of 80% (36/45), and a normalized area under the receiver operating characteristic curve, Az=0.84. Combining the strain features with the B-mode features obtained a significantly better Az=0.93, p-value
AB - Background: Elastography is a new sonographic imaging technique to acquire the strain information of tissues and transform the information into images. Radiologists have to observe the gray-scale distribution of tissues on the elastographic image interpreted as the reciprocal of Young[U+05F3]s modulus to evaluate the pathological changes such as scirrhous carcinoma. In this study, a computer-aided diagnosis (CAD) system was developed to extract quantitative strain features from elastographic images to reduce operator-dependence and provide an automatic procedure for breast mass classification. Method: The collected image database was composed of 45 malignant and 45 benign breast masses. For each case, tumor segmentation was performed on the B-mode image to obtain tumor contour which was then mapped to the elastographic images to define the corresponding tumor area. The gray-scale pixels around tumor area were classified into white, gray, and black by fuzzy c-means clustering to highlight stiff tissues with darker values. Quantitative strain features were then extracted from the black cluster and compared with the B-mode features in the classification of breast masses. Results: The performance of the proposed strain features achieved an accuracy of 80% (72/90), a sensitivity of 80% (36/45), a specificity of 80% (36/45), and a normalized area under the receiver operating characteristic curve, Az=0.84. Combining the strain features with the B-mode features obtained a significantly better Az=0.93, p-value
KW - B-mode
KW - Breast cancer
KW - Computer-aided diagnosis
KW - Elastography
KW - Fuzzy c-means clustering
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U2 - 10.1016/j.compbiomed.2015.06.013
DO - 10.1016/j.compbiomed.2015.06.013
M3 - Article
C2 - 26159906
AN - SCOPUS:84936856193
SN - 0010-4825
VL - 64
SP - 91
EP - 100
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
ER -