Between-class variance as a measure of image bimodality and sharpness

Author

Demirkaya, Omer

Author_Institution

Dept. of Physiol. & Biophys., Mayo Clinic, Rochester, MN, USA

Volume

2

fYear

1997

fDate

30 Oct-2 Nov 1997

Firstpage

590

Abstract

This paper investigates the suitability of between-class variance as a measure of image bimodality and sharpness. Between-class variance has been first proposed as a criterion function to determine an optimal threshold to segment images into nearly uniform regions. The evaluation on artificial images and real images of human aortic sections showed that normalized between-class variance can be effectively used to detect image bimodality, as well as to evaluate image sharpness. While the former can be utilized in image segmentation, the latter is essential to ensure repeatability in quantitative microscopy

Keywords

blood vessels; image segmentation; medical image processing; optical microscopy; artificial images; autofocusing; between-class variance; human aortic sections; image bimodality; image sharpness; nearly uniform regions; normalized between-class variance; optimal threshold; quantitative microscopy; real images; repeatability; Biophysics; Equations; Histograms; Humans; Image segmentation; Mean square error methods; Physiology; Pixel; Probability distribution; Quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1094-687X

Print_ISBN

0-7803-4262-3

Type

conf

DOI

10.1109/IEMBS.1997.757679

Filename

757679