Content based image retrieval from chest radiography databases

Authors:
Shengwen Guo;Jinshan Tang
Affiliations:
System Research Institute & Department of Advanced Technologies, Alcorn State University, Lorman, MS and Department of Biomedical Engineering, South China University of Technology, Guangzhou, ...;System Research Institute & Department of Advanced Technologies, Alcorn State University, Lorman, MS
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

Citing 10
Cited 0

Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases

Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Knowledge-Based Image Retrieval with Spatial and Temporal Constructs

IEEE Transactions on Knowledge and Data Engineering
Fast and Effective Retrieval of Medical Tumor Shapes

IEEE Transactions on Knowledge and Data Engineering
Non-rigid point matching: algorithms, extensions and applications

Non-rigid point matching: algorithms, extensions and applications
Relevance Feedback for Spine X-ray Retrieval

CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Pre-Indexing for Fast Partial Shape Matching of Vertebrae Images

CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Content-based retrieval of dynamic PET functional images

IEEE Transactions on Information Technology in Biomedicine
Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework

IEEE Transactions on Information Technology in Biomedicine
Color and texture descriptors

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Content-based image retrieval (CBIR) has the potential to provide medical doctors with a powerful resource to help make accurate diagnosis. The main goal of CBIR is to efficiently retrieve images that are visually similar to a query image. In this paper we focus on CBIR from chest x-ray image databases. We describe a novel approach for extraction of region of interest (ROI), which is a bounding box of lung fields. The region is achieved by 3×3 key points based on density distribution of the chest x-ray image. Then we propose an elastic reformation registration algorithm based on the control points. A multi-dimensional feature space is used to represent the image content, including texture, edge, and descriptors based on filtering. Experiments results demonstrate that our approach can retrieve visually similar images effectively and increase the diagnostic accuracy.