Foetus ultrasound medical image segmentation via variational level set algorithm

Choong, M. Y. and Seng, M. C. and Yang, Soo Siang and Aroland, McOnie Jilui Kiring and Teo, Kenneth Tze Kin (2012) Foetus ultrasound medical image segmentation via variational level set algorithm. In: 3rd International Conference on Intelligent Systems, Modelling and Simulation (ISMS 2012) , 8-10 February 2012, Kota Kinabalu, Sabah, Malaysia.

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/ISMS.2012.102

Abstract

Abstract—There is a challenge to segment the medical image which is often blurred and consists of noise. The objects to be segmented are always changing shape. Thus, there is a need to apply a method to automated segment well the objects for future analysis without any assumptions about the object’s topology are made. In general, when performing pregnancy ultrasound scanning, obstetrician needs to find out the best position or angle of the foetus and freeze the scene. The obstetrician will click on the crown and the rump of the foetus to get the foetus length. The segmentation technique applied is level set method. A variational level set algorithm has been successfully implemented in medical image segmentation (Xray image, MRI image and ultrasound image). The results showed the level set contour evolved well on the low contrast and noise consisting medical image, especially the ultrasound image.

Item Type:Conference Paper (UNSPECIFIED)
Uncontrolled Keywords:Image segmentation, Level set algorithms, Foetus ultrasound medical image
Subjects:?? R855-855.5 ??
Q Science > QA Mathematics
Divisions:SCHOOL > School of Engineering and Information Technology
ID Code:4553
Deposited By:IR Admin
Deposited On:16 Jul 2012 10:25
Last Modified:08 Sep 2014 12:43

Repository Staff Only: item control page


Browse Repository
Collection
   Articles
   Book
   Speeches
   Thesis
   UMS News
Search
Quick Search

   Latest Repository

Link to other Malaysia University Institutional Repository

Malaysia University Institutional Repository