An Improved Image Segmentation Algorithm Based on MET Method
Image segmentation is a basic component of many computer vision systems and pattern recognition. Thresholding is a simple but effective method to separate objects from the background. A commonly used method, Kittler and Illingworth\'s minimum error thresholding (MET), improves the image segmentation effect obviously. Its simpler and easier to implement. However, it fails in the presence of skew and heavy-tailed class-conditional distributions or if the histogram is unimodal or close to unimodal. The Fisher information (FI) measure is an important concept in statistical estimation theory and information theory. Employing the FI measure, an improved threshold image segmentation algorithm FI-based extension of MET is developed. Comparing with the MET method, the improved method in general can achieve more robust performance when the data for either class is skew and heavy-tailed.
Keywords: Image segmentation; Image thresholding; Minimum error thresholding (MET); Fisher information; Information theory.
Download Full-Text
ABOUT THE AUTHORS
Z. A. Abo-Eleneen
1 College of Computer& Informatics, Zagazig University, Zagazig 44519, Egypt 2College of Sciences, , Qassim University. PO Box 6688, 51452 Buraydah, Saudi Arabia.
Gamil Abdel-Azim
College of Computer& Informatics, Canal Suez University, Egypt
Z. A. Abo-Eleneen
1 College of Computer& Informatics, Zagazig University, Zagazig 44519, Egypt 2College of Sciences, , Qassim University. PO Box 6688, 51452 Buraydah, Saudi Arabia.
Gamil Abdel-Azim
College of Computer& Informatics, Canal Suez University, Egypt
