title: Quantitative analysis of breast echotexture patterns in automated breast ultrasound images.
abstract: OBJECTIVE
  Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images.
  
  
  METHODS
  A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c-mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave-one-out cross-validation method was utilized as the classifier. The authors' database included a total of 208 ABUS images of the breasts of 104 females.
  
  
  RESULTS
  The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814.
  
  
  CONCLUSIONS
  The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future.
authors.affiliation: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
authors.affiliation: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Yu-Ling
authors.initials: YL
authors.lastname: Hou
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Chung-Ming
authors.initials: CM
authors.lastname: Lo
authors.affiliation: Department of Surgery, National Taiwan University Hospital, Taipei 10617, Taiwan.
authors.forename: Chiun-Sheng
authors.initials: CS
authors.lastname: Huang
authors.affiliation: Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan and Tu and Yuen Center for Functional Onco-Imaging and Department of Radiological Science, University of California, Irvine, California 92697.
authors.forename: Jeon-Hor
authors.initials: JH
authors.lastname: Chen
authors.affiliation: Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
authors.forename: Won Hwa
authors.initials: WH
authors.lastname: Kim
authors.affiliation: Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
authors.forename: Jung Min
authors.initials: JM
authors.lastname: Chang
authors.affiliation: Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
authors.forename: Min Sun
authors.initials: MS
authors.lastname: Bae
authors.affiliation: Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
authors.forename: Woo Kyung
authors.initials: WK
authors.lastname: Moon
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult