Title

An Automated Pathological Class Level Annotation System for Volumetric Brain Images

Publication Type

Conference Proceeding Article

Publication Date

12-2012

Abstract

We introduce an automated, pathological class level annotation system for medical volumetric brain images. While much of the earlier work has mainly focused on annotating regions of interest in medical images, our system does not require annotated region level training data nor assumes perfect segmentation results for the regions of interest; the time and effort needed for acquiring training data are hence significantly reduced. This capability of handling high-dimensional noisy data, however, poses additional technical challenges, since statistical estimation of models for such data is prone to over-fitting. We propose a framework that combines a regularized logistic regression method and a kernel-based discriminative method to address these problems. Regularized methods provide a flexible selection mechanism that is well-suited for high dimensional noisy data. Our experiments show promising results in classifying computer tomography images of traumatic brain injury patients into pathological classes.

Discipline

Computer Sciences | Health Information Technology

Research Areas

Intelligent Systems and Decision Analytics

Publication

American Medical Informatics Association Annual Symposium (AMIA)

Volume

2012

First Page

1201

Last Page

1210

ISBN

1942-597X

City or Country

Chicago, IL, USA