Characterizing non-linear dependencies among pairs of clinical variables and imaging data.

ABSTRACT: Advances in computer-aided diagnosis (CAD) systems have shown the benefits of using computer-based techniques to obtain quantitative image measurements of the extent of a particular disease. Such measurements provide more accurate information that can be used to better study the associations between anatomical changes and clinical findings. Unfortunately, even with the use of quantitative image features, the correlations between anatomical changes and clinical findings are often not apparent and definite conclusions are difficult to reach. This paper uses nonparametric exploration techniques to demonstrate that even when the associations between two-variables seems weak, advanced properties of the associations can be studied and used to better understand the relationships between individua

SUBMITTER: Caban JJ 

PROVIDER: S-EPMC3561932 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature