title: Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography.
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'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.
  
  
  METHODS
  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.
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Chung-Ming
authors.initials: CM
authors.lastname: Lo
authors.affiliation: Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
authors.forename: Yeun-Chung
authors.initials: YC
authors.lastname: Chang
authors.affiliation: Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
authors.forename: Ya-Wen
authors.initials: YW
authors.lastname: Yang
authors.affiliation: Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. Electronic address: huangcs@ntu.edu.tw.
authors.forename: Chiun-Sheng
authors.initials: CS
authors.lastname: Huang
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. Electronic address: rfchang@csie.ntu.edu.tw.
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D062005:Biopsy, Large-Core Needle; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D016000:Cluster Analysis; D054459:Elasticity Imaging Techniques / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D055815:Young Adult
keywords: B-mode; Breast cancer; Computer-aided diagnosis; Elastography; Fuzzy c-means clustering