Database Design and Implementation for Quantitative Image Analysis Research

TLDR

Quantitative image analysis extends beyond visual assessment by providing computer‑derived measurements after segmentation of anatomical regions, yet existing PACS are optimized for storage rather than efficient handling of such quantitative data. The paper introduces a system that combines image segmentation, quantitation, and characterization with database and data‑mining capabilities. The authors develop generic process and data models grounded in the DICOM hierarchy, augmenting it with tables for segmentation results and quantitative data, and implement the database in PostgreSQL on a UNIX server while describing statistical data‑mining queries. Two quantitative imaging experiments—lung‑cancer screening and emphysema assessment—demonstrate that the system can manage large volumes of quantitative data essential for research, development, and deployment of computer‑aided diagnosis tools.

Abstract

Quantitative image analysis (QIA) goes beyond subjective visual assessment to provide computer measurements of the image content, typically following image segmentation to identify anatomical regions of interest (ROIs). Commercially available picture archiving and communication systems focus on storage of image data. They are not well suited to efficient storage and mining of new types of quantitative data. In this paper, we present a system that integrates image segmentation, quantitation, and characterization with database and data mining facilities. The paper includes generic process and data models for QIA in medicine and describes their practical use. The data model is based upon the Digital Imaging and Communications in Medicine (DICOM) data hierarchy, which is augmented with tables to store segmentation results (ROIs) and quantitative data from multiple experiments. Data mining for statistical analysis of the quantitative data is described along with example queries. The database is implemented in PostgreSQL on a UNIX server. Database requirements and capabilities are illustrated through two quantitative imaging experiments related to lung cancer screening and assessment of emphysema lung disease. The system can manage the large amounts of quantitative data necessary for research, development, and deployment of computer-aided diagnosis tools.