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Proceedings Paper

A hierarchical SVG image abstraction layer for medical imaging
Author(s): Edward Kim; Xiaolei Huang; Gang Tan; L. Rodney Long; Sameer Antani
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Paper Abstract

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.

Paper Details

Date Published: 11 March 2010
PDF: 9 pages
Proc. SPIE 7628, Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 762809 (11 March 2010); doi: 10.1117/12.844502
Show Author Affiliations
Edward Kim, Lehigh Univ. (United States)
Xiaolei Huang, Lehigh Univ. (United States)
Gang Tan, Lehigh Univ. (United States)
L. Rodney Long, National Institutes of Health (United States)
Sameer Antani, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 7628:
Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications
Brent J. Liu; William W. Boonn, Editor(s)

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