Feature selection and classifiers for the computerized detection of mass lesions in digital mammography

Author

Kupinski, Matthew A. ; Giger, M.L.

Author_Institution

Dept. of Radiol., Chicago Univ., IL, USA

Volume

4

fYear

1997

fDate

9-12 Jun 1997

Firstpage

2460

Abstract

We have investigated various methods of feature selection for two different data classifiers used in the computerized detection of mass lesions in digital mammograms. Numerous features were extracted from abnormal and normal breast regions from a database consisting of 210 individual mammograms. A step-wise method, a genetic algorithm and individual feature analysis were employed to select a subset of features to be used with linear discriminants. Similar techniques were also employed for an artificial neural network classifier. In both tests the genetic algorithm was able to either outperform or equal the performance of other methods

Keywords

diagnostic radiography; feature extraction; genetic algorithms; image classification; medical image processing; neural nets; artificial neural network classifier; breast regions; breast scanning; data classifiers; digital mammography; feature selection; genetic algorithm; individual feature analysis; linear discriminants; mass lesion detection; step-wise method; Algorithm design and analysis; Artificial neural networks; Breast; Data mining; Feature extraction; Genetic algorithms; Image databases; Lesions; Spatial databases; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.614543

Filename

614543