title: A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.
abstract: OBJECTIVE
  Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses.
  
  
  METHODS
  An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method.
  
  
  RESULTS
  Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized using other three feature selection methods. In addition, among 271 features, the shape, local morphological features, fat and calcification based features were the most frequently selected features to build ANNs.
  
  
  CONCLUSIONS
  Although conventional GA is a powerful tool in optimizing classifiers used in CAD schemes of medical images, it is very computationally intensive. This study demonstrated that using a new SFFS based approach enabled to significantly improve efficacy of image feature selection for developing CAD schemes.
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019.
authors.forename: Maxine
authors.initials: M
authors.lastname: Tan
authors.affiliation: Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213.
authors.forename: Jiantao
authors.initials: J
authors.lastname: Pu
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 and Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D066264:Datasets as Topic; D006801:Humans; D008327:Mammography / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013997:Time Factors
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