Conference proceeding
A Hierarchical SVG Image Abstraction Layer for Medical Imaging
MEDICAL IMAGING 2010: ADVANCED PACS-BASED IMAGING INFORMATICS AND THERAPEUTIC APPLICATIONS, v 7628(1), pp 762809-762809
10 Mar 2010
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.
Metrics
6 Record Views
5 citations in Scopus
Details
- Title
- A Hierarchical SVG Image Abstraction Layer for Medical Imaging
- Creators
- Edward Kim - Lehigh UniversityXiaolei Huang - Lehigh UniversityGang Tan - Lehigh UniversityL. Rodney Long - National Institutes of HealthSameer Antani - National Institutes of Health
- Contributors
- B J Liu (Editor)W W Boonn (Editor)
- Publication Details
- MEDICAL IMAGING 2010: ADVANCED PACS-BASED IMAGING INFORMATICS AND THERAPEUTIC APPLICATIONS, v 7628(1), pp 762809-762809
- Series
- Proceedings of SPIE
- Publisher
- Spie-Int Soc Optical Engineering
- Number of pages
- 9
- Grant note
- IIS-0812120 / NSF; National Science Foundation (NSF) NIHNLM- HHSN276200900722P / DHHS
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000284752500007
- Scopus ID
- 2-s2.0-77953413437
- Other Identifier
- 991021884588304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Medical Informatics
- Optics
- Radiology, Nuclear Medicine & Medical Imaging