Label fusion based brain MR image segmentation via a latent selective model

Gang Liu; Xiantang Guo; Kai Zhu; Hengxu Liao

doi:10.1117/12.2302775

10 April 2018 Label fusion based brain MR image segmentation via a latent selective model

Gang Liu, Xiantang Guo, Kai Zhu, Hengxu Liao

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151X (2018) https://doi.org/10.1117/12.2302775
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

Citation Download Citation

Gang Liu, Xiantang Guo, Kai Zhu, and Hengxu Liao "Label fusion based brain MR image segmentation via a latent selective model", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151X (10 April 2018); https://doi.org/10.1117/12.2302775

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Image fusion

Expectation maximization algorithms

Detection and tracking algorithms

Magnetic resonance imaging

Image processing algorithms and systems

Brain

Show All Keywords

Keywords/Phrases

Search In:

Publication Years