Signal Detection Using Support Vector Machines in the Presence of Ultrasonic Speckle

C. Kotropoulos and I. Pitas

SPIE 2002 Medical Imaging, San Diego California, February 2002.

Abstract: Support Vector Machines 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 support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B­mode images as well as clinical ultrasonic images.