title: Computer-aided classification of mammographic masses using visually sensitive image features.
abstract: To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms.
  
  
  
  An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score.
  
  
  
  Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025.
  
  
  
  This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a "visual aid" interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features.
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
authors.forename: Yunzhi
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
authors.forename: Faranak
authors.initials: F
authors.lastname: Aghaei
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
authors.forename: Ali
authors.initials: A
authors.lastname: Zarafshani
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
authors.forename: Yuchen
authors.initials: Y
authors.lastname: Qiu
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas, El Paso, TX, USA.|Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Qian
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001940:Breast / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*
keywords: Computer-aided diagnosis (CAD); classification of mammographic masses; quantification of visually sensitive image features; quantitative image feature selection in CAD