A hierarchical SVG image abstraction layer for medical imaging

Edward Kim, Sharon Xiaolei Huang, Gang Tan, L. Rodney Long, Sameer Antani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2010 - Advanced PACS-based Imaging Informatics and Therapeutic Applications
DOIs
Publication statusPublished - Jun 17 2010
EventMedical Imaging 2010 - Advanced PACS-based Imaging Informatics and Therapeutic Applications - San Diego, CA, United States
Duration: Feb 17 2010Feb 18 2010

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7628
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2010 - Advanced PACS-based Imaging Informatics and Therapeutic Applications
CountryUnited States
CitySan Diego, CA
Period2/17/102/18/10

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All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Kim, E., Huang, S. X., Tan, G., Long, L. R., & Antani, S. (2010). A hierarchical SVG image abstraction layer for medical imaging. In Medical Imaging 2010 - Advanced PACS-based Imaging Informatics and Therapeutic Applications [762809] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7628). https://doi.org/10.1117/12.844502