Neural network scoring of spots in X-Gal and -leu plates

Author

Jafari-Khonzani, K. ; Soltanian-Zadeh, H. ; Finley, R.L., Jr. ; Fotouhi, F.

Author_Institution

Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA

fYear

2005

fDate

26-28 June 2005

Firstpage

46

Lastpage

50

Abstract

We have developed an image analysis system for scoring yeast growth and color development in images of 96-well plates, a common format for high throughput assays. We use a segmentation method to locate the plates and spots. Color histogram and wavelet features are extracted respectively from spots of X-Gal and -leu plates. Two artificial neural networks are separately employed to score spots on each plate. The performance of the system is evaluated using a data set of 50 images. The data set was divided into 25 training and 25 testing images. Accuracies of 99.7% and 95.2% have been achieved for scoring the X-Gal and -leu plates respectively.

Keywords

biology; feature extraction; image colour analysis; image segmentation; neural nets; -leu plates; X-Gal; color histogram; image analysis system; image color development; image segmentation; neural network scoring; wavelet features; yeast growth scoring; Artificial neural networks; Data mining; Feature extraction; Fungi; Histograms; Image color analysis; Image segmentation; Neural networks; Testing; Throughput;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American

Print_ISBN

0-7803-9187-X

Type

conf

DOI

10.1109/NAFIPS.2005.1548505

Filename

1548505