Segmentation of ultrasonic images using Support Vector Machines

被引:45
作者
Kotropoulos, C [1 ]
Pitas, I [1 ]
机构
[1] Aristotle Univ Thessaloniki, Dept Informat, GR-54006 Thessaloniki, Greece
关键词
ultrasound B-mode images; lesion detection; segmentation; support vector machines; radial basis function kernel;
D O I
10.1016/S0167-8655(02)00177-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:715 / 727
页数:13
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