PMID: 8946365Oct 1, 1996Paper

Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue

Medical Physics
B SahinerM M Goodsitt

Abstract

We investigated a new approach to feature selection, and demonstrated its application in the task of differentiating regions of interest (ROIs) on mammograms as either mass or normal tissue. The classifier included a genetic algorithm (GA) for image feature selection, and a linear discriminant classifier or a backpropagation neural network (BPN) for formulation of the classifier outputs. The GA-based feature selection was guided by higher probabilities of survival for fitter combinations of features, where the fitness measure was the area Az under the receiver operating characteristic (ROC) curve. We studied the effect of different GA parameters on classification accuracy, and compared the results to those obtained with stepwise feature selection. The data set used in this study consisted of 168 ROIs containing biopsy-proven masses and 504 ROIs containing normal tissue. From each ROI, a total of 587 features were extracted, of which 572 were texture features and 15 were morphological features. The GA was trained and tested with several different partitionings of the ROIs into training and testing sets. With the best combination of the GA parameters, the average test Az value using a linear discriminant classifier reached 0.90, ...Continue Reading

Citations

Jun 7, 2005·Ultrasound in Medicine & Biology·Wei-Ming ChenDar-Ren Chen
Dec 7, 2002·Ultrasound in Medicine & Biology·Dar-Ren ChenYu-Len Huang
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