Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography
Journal
Computers in Biology and Medicine
Journal Volume
64
Pages
91-100
Date Issued
2015
Author(s)
Abstract
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<0.05. Conclusions: Summarily, the quantified strain features can be combined with the B-mode features to provide a promising suggestion in distinguishing malignant from benign tumors. ? 2015 Elsevier Ltd.
Subjects
B-mode; Breast cancer; Computer-aided diagnosis; Elastography; Fuzzy c-means clustering
SDGs
Other Subjects
Diagnosis; Fuzzy systems; Histology; Image segmentation; Imaging techniques; Medical imaging; Tissue; Tumors; Automatic procedures; Breast Cancer; Computer Aided Diagnosis(CAD); Elastography; Fuzzy C means clustering; Operator dependence; Pathological changes; Receiver operating characteristic curves; Computer aided diagnosis; adult; aged; Article; breast carcinoma; breast papilloma; breast tumor; computer aided design; diagnostic accuracy; diagnostic test accuracy study; disease classification; elastography; female; human; human tissue; image processing; intraductal carcinoma; major clinical study; needle biopsy; priority journal; real time ultrasound scanner; tumor diagnosis; tumor volume; very elderly; adolescent; breast tumor; cluster analysis; computer assisted diagnosis; echography; elastography; fuzzy logic; large core needle biopsy; middle aged; pathology; procedures; young adult; Adolescent; Adult; Aged; Aged, 80 and over; Biopsy, Large-Core Needle; Breast Neoplasms; Cluster Analysis; Elasticity Imaging Techniques; Female; Fuzzy Logic; Humans; Image Interpretation, Computer-Assisted; Middle Aged; Young Adult
Publisher
Elsevier Ltd
Type
journal article