Proceedings Article | 11 March 2010
KEYWORDS: Image segmentation, Visualization, Medical imaging, Classification systems, Feature extraction, Image classification, Computer programming, Image retrieval, Standards development, Picture Archiving and Communication System
As medical imaging rapidly expands, there is an increasing need to structure and organize image data for
efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based
image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring
information to bridge the "semantic gap", a disparity between machine and human image understanding. An
additional consideration in medical images is the organization and integration of clinical diagnostic information.
As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using
an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and
clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any
feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can
be combined in our abstraction with high level descriptions or classifications. And our representation can natively
characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.
