title: Feature selection for the automated detection of metaphase chromosomes: performance comparison using a receiver operating characteristic method.
abstract: BACKGROUND
  The purpose of this study is to identify a set of features for optimizing the performance of metaphase chromosome detection under high throughput scanning microscopy. In the development of computer-aided detection (CAD) scheme, feature selection is critically important, as it directly determines the accuracy of the scheme. Although many features have been examined previously, selecting optimal features is often application oriented.
  
  
  METHODS
  In this experiment, 200 bone marrow cells were first acquired by a high throughput scanning microscope. Then 9 different features were applied individually to group captured images into the clinically analyzable and unanalyzable classes. The performance of these different methods was assessed by a receiving operating characteristic (ROC) method.
  
  
  RESULTS
  The results show that using the number of labeled regions on each acquired image is suitable for the first on-line CAD scheme. For the second off-line CAD scheme, it would be suggested to combine four feature extraction methods including the number of labeled regions, average regions area, average region pixel value, and the standard deviation of either region distance or circularity.
  
  
  CONCLUSIONS
  This study demonstrates an effective method of feature selection and comparison to facilitate the optimization of the CAD schemes for high throughput scanning microscope in the future.
authors.affiliation: Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Boulevard, Norman, OK 73019, USA.
authors.forename: Yuchen
authors.initials: Y
authors.lastname: Qiu
authors.affiliation: Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Boulevard, Norman, OK 73019, USA ; Department of Biology, Mudanjiang Medical University, Mudanjiang 157011, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Song
authors.affiliation: Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
authors.forename: Xianglan
authors.initials: X
authors.lastname: Lu
authors.affiliation: Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Boulevard, Norman, OK 73019, USA.
authors.forename: Yuhua
authors.initials: Y
authors.lastname: Li
authors.affiliation: Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Boulevard, Norman, OK 73019, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
authors.affiliation: Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
authors.forename: Shibo
authors.initials: S
authors.lastname: Li
authors.affiliation: Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Boulevard, Norman, OK 73019, USA.
authors.forename: Hong
authors.initials: H
authors.lastname: Liu
mesh_terms: D019540:Area Under Curve; D002875:Chromosomes / Q000648:ultrastructure*; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008677:Metaphase; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve