title: Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model.
abstract: OBJECTIVE
  Improving radiologists' performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification.
  
  
  METHODS
  We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest, we performed the study using a tenfold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only.
  
  
  RESULTS
  The area under the receiver operating characteristic curve [Formula: see text] was obtained for the classification task. The results also showed that the most frequently selected features by the SFFS-based algorithm in tenfold iterations were those related to mass shape, isodensity, and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions.
  
  
  CONCLUSIONS
  In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a "second reader" in future clinical practice.
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA. Maxine.Y.Tan-1@ou.edu.
authors.forename: Maxine
authors.initials: M
authors.lastname: Tan
authors.affiliation: Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
authors.forename: Jiantao
authors.initials: J
authors.lastname: Pu
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.|Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D009367:Neoplasm Staging; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D060388:Support Vector Machine
keywords: Breast cancer; Computer-aided diagnosis of mammograms; Feature selection; Pattern classification