3D segmentation of medical volume image using hybrid level set method

3D Segmentation with an Application of Level Set-Method using MRI Volumes for Image Guided Surgery

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2007.4353529 ◽

2007 ◽

Cited By ~ 6

Author(s):

A. Bosnjak ◽

G. Montilla ◽

R. Villegas ◽

I. Jara

Keyword(s):

Level Set Method ◽

Level Set ◽

Image Guided Surgery ◽

3D Segmentation ◽

Guided Surgery ◽

Image Guided

Download Full-text

3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation

Journal of Healthcare Engineering ◽

10.1155/2018/7097498 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15

Author(s):

Chuin-Mu Wang ◽

Chieh-Ling Huang ◽

Sheng-Chih Yang

Keyword(s):

Image Segmentation ◽

Level Set Method ◽

Level Set ◽

Three Dimensional ◽

Breast Mri ◽

Medical Image Segmentation ◽

Tumor Segmentation ◽

3D Segmentation ◽

3D Shape ◽

Segmentation Methods

Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations. A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume. Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins. To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases. Subsequently, we simultaneously compared various existing 3D segmentation methods. The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images. In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods.

Download Full-text