Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2112
Title: Improved Segmentation of Speckled Images Using an Arithmetic-to-Geometric Mean Ratio Kernel
Authors: Daba, Jihad S. 
Affiliations: Department of Electrical Engineering 
Keywords: Discriminant function
False alarm
Segmentation
Signal-to-noise ratio
Skewness
Subjects: Speckle
Issue Date: 2007
Part of: International journal of electrical robotics, electronics and communications engineering
Volume: 1
Issue: 10
Start page: 1454
End page: 1457
Abstract: 
In this work, we improve a previously developed segmentation scheme aimed at extracting edge information from speckled images using a maximum likelihood edge detector. The scheme was based on finding a threshold for the probability density function of a new kernel defined as the arithmetic mean-to-geometric mean ratio field over a circular neighborhood set and, in a general context, is founded on a likelihood random field model (LRFM). The segmentation algorithm was applied to discriminated speckle areas obtained using simple elliptic discriminant functions based on measures of the signal-to-noise ratio with fractional order moments. A rigorous stochastic analysis was used to derive an exact expression for the cumulative density function of the probability density function of the random field. Based on this, an accurate probability of error was derived and the performance of the scheme was analysed. The improved segmentation scheme performed well for both simulated and real images and showed superior results to those previously obtained using the original LRFM scheme and standard edge detection methods. In particular, the false alarm probability was markedly lower than that of the original LRFM method with oversegmentation artifacts virtually eliminated. The importance of this work lies in the development of a stochastic-based segmentation, allowing an accurate quantification of the probability of false detection. Non visual quantification and misclassification in medical ultrasound speckled images is relatively new and is of interest to clinicians.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2112
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

Show full item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.