Segmentation of brain MR images: a self-adaptive online vector quantization approach

Lihong Li; Dongqing Chen; Hongbing Lu; Zhengrong Liang

doi:10.1117/12.431024

3 July 2001 Segmentation of brain MR images: a self-adaptive online vector quantization approach

Lihong Li, Dongqing Chen, Hongbing Lu, Zhengrong Liang

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431024
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

We present a fully automatic algorithm for brain magnetic resonance (MR) image segmentation. The three-dimensional (3D) volumetric MR dataset is first interpolated for an adequate local intensity vector on each voxel. Then a morphology dilation filter and region growing technique are applied to extract the region of brain volume, strapping away the skull, scalp and other tissues. The principal component analysis (PCA) is utilized to generate a series of feature vectors from the local vectors via the Karhunen-Loeve (K-L) transformation for those voxels within the extracted region. We choose those first few principal components that sum up to, at least, 90% percent of the total variance for optimizing the dimensions of the feature vectors. Then a modified self-adaptive online vector quantization algorithm is applied to these feature vectors for classification. The presented algorithm requires no prior knowledge of the data distribution except a maximum number of distinct groups for classification, which can be set based on anatomical knowledge. Numerical analysis of the algorithm and experimental tests on brain MR images are presented. Results demonstrate efficient, robust, and self-adaptive properties of the presented algorithm.

Citation Download Citation

Lihong Li, Dongqing Chen, Hongbing Lu, and Zhengrong Liang "Segmentation of brain MR images: a self-adaptive online vector quantization approach", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431024

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