Please use this identifier to cite or link to this item:
Title: Detection of ultrasonic images in the presence of a random number of scatterers : a statistical learning approach
Authors: Daba, Jihad S. 
Abdullatif, O.M
Affiliations: Department of Electrical Engineering 
Keywords: LS-SVM
Medical Ultrasound Imaging
Partially Developed Speckle
Multi-Look Model
Issue Date: 2007
Part of: Journal of the world academy of science engineering and technology
Volume: 1
Issue: 12
Start page: 542
End page: 545
Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

Show full item record

Record view(s)

checked on Dec 4, 2021

Google ScholarTM


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