Segmentation of ultrasonic images using support vector machines

Authors:
Constantine Kotropoulos;Ioannis Pitas
Affiliations:
Department of Informatics, Aristotle University of Thessaloniki, Box 451, Thessaloniki 540 06, Greece;Department of Informatics, Aristotle University of Thessaloniki, Box 451, Thessaloniki 540 06, Greece
Venue:
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Year:
2003

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Abstract

Support Vector Machines (SVMs) are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of SVMs to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained SVMs with a radial basis function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.