Brain abnormality segmentation based on l1-norm minimization

Ke Zeng; Guray Erus; Manoj Tanwar; Christos Davatzikos

doi:10.1117/12.2043146

21 March 2014 Brain abnormality segmentation based on l₁-norm minimization

Ke Zeng, Guray Erus, Manoj Tanwar, Christos Davatzikos

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 903409 (2014) https://doi.org/10.1117/12.2043146
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

We present a method that uses sparse representations to model the inter-individual variability of healthy anatomy from a limited number of normal medical images. Abnormalities in MR images are then defined as deviations from the normal variation. More precisely, we model an abnormal (pathological) signal y as the superposition of a normal part ~y that can be sparsely represented under an example-based dictionary, and an abnormal part r. Motivated by a dense error correction scheme recently proposed for sparse signal recovery, we use l₁- norm minimization to separate ~y and r. We extend the existing framework, which was mainly used on robust face recognition in a discriminative setting, to address challenges of brain image analysis, particularly the high dimensionality and low sample size problem. The dictionary is constructed from local image patches extracted from training images aligned using smooth transformations, together with minor perturbations of those patches. A multi-scale sliding-window scheme is applied to capture anatomical variations ranging from fine and localized to coarser and more global. The statistical significance of the abnormality term r is obtained by comparison to its empirical distribution through cross-validation, and is used to assign an abnormality score to each voxel. In our validation experiments the method is applied for segmenting abnormalities on 2-D slices of FLAIR images, and we obtain segmentation results consistent with the expert-defined masks.

Citation Download Citation

Ke Zeng, Guray Erus, Manoj Tanwar, and Christos Davatzikos "Brain abnormality segmentation based on l₁-norm minimization", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903409 (21 March 2014); https://doi.org/10.1117/12.2043146

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