By Topic

Multi-kernel SVM based classification for tumor segmentation by fusion of MRI images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Nan Zhang ; Grad. Sch. Shen Zhen, Tsinghua Univ., Tsinghua ; Qingmin Liao ; Ruan, Su ; Lebonvallet, S.
more authors

Tumor segmentation, a significant application in the field of medical imaging and pattern recognition, is still a very difficult and unsolved problem up to now. In this paper, an improved SVM algorithm-multi-kernel SVM, integrated with data fusion process, is proposed to segment the tumors from the MRI image sequence. Three kinds of MRI image sequence-T2, PD, FLAIR are used as input sources in learning and classifying process. Then a region growing step is exploited for a refinement of the tumor contour. At last, according to the follow-up result of the same patient at five different periods, it is obvious that the tumor's volume becomes smaller, and an evaluation percentage is given to prove the effectiveness of the therapy. The quantification of result demonstrates the effectiveness of the proposed method.

Published in:

Imaging Systems and Techniques, 2009. IST '09. IEEE International Workshop on

Date of Conference:

11-12 May 2009