An integrated segmentation and visualization tool for MR brain image processing

22 March 2007 An integrated segmentation and visualization tool for MR brain image processing

Audrey H. Zhuang, Daniel J. Valentino, Val Stambolstian, Ivo Dinov, Arthur W. Toga

Author Affiliations +

Proceedings Volume 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures; 65092N (2007) https://doi.org/10.1117/12.710457
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

The automated segmentation of brain structures is an important step in many neuroimaging analyses. A variety of automated segmentation tools exist, however, most segmentation results are imperfect, and require manual editing of the resulting contours or surfaces. A new, integrated segmentation and visualization tool, the LONI Anatomist, was developed to provide an open architecture for applying automated segmentation algorithms and interactive tools to manually edit the automated segmentation results. Two automated segmentation algorithms were developed to skullstrip MR brain images and were integrated in the LONI Anatomist: a two-dimensional model-based level set (2D MLS) algorithm and a three-dimensional MLS algorithm. These MLS algorithms were based on the Level Set methods by incorporating two constraints into the level set framework to evolve the zero level set surface in 2D space and 3D space respectively. In the LONI Anatomist, the evolution of the level set was displayed in real time, and final results were corrected using easy-to-use interactive editing tools. Additional tools were provided to visualize the results, such as color overlays of 2D contours over the original gray-scale slices, 3D surface visualization, etc. The LONI Anatomist was implemented in Java using a portable imaging framework (the jViewbox) for medical image display and manipulation, using the Java Image I/O plug-ins for reading/writing DICOM, MINC, ANALYZE image files, and using the Java Advanced Imaging classes for image processing. The design of the system provides a framework for researchers to integrate more mathematical algorithms for converting the algorithms into practical use.

Citation Download Citation

Audrey H. Zhuang, Daniel J. Valentino, Val Stambolstian, Ivo Dinov, and Arthur W. Toga "An integrated segmentation and visualization tool for MR brain image processing", Proc. SPIE 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures, 65092N (22 March 2007); https://doi.org/10.1117/12.710457

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
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Brain

Neuroimaging

Algorithm development

Visualization

Magnetic resonance imaging

Image processing algorithms and systems

Show All Keywords

Keywords/Phrases

Search In:

Publication Years