Size-Invariant Descriptors for Detecting Regions of Abnormal Growth in Cervical Vertebrae

Abstract

Digitized spinal X-ray images exhibiting specific pathological conditions such as osteophytes can be retrieved from large databases using Content Based Image Retrieval (CBIR) techniques. For efficient image retrieval, it is important that the pathological features of interest be detected with high accuracy. In this study, new size-invariant features were investigated for the detection of anterior osteophytes, including claw and traction in cervical vertebrae. Using a K-means clustering and nearest neighbor classification approach, average correct classification rates of 85.80%, 86.04% and 84.44% were obtained for claw, traction and anterior osteophytes, respectively.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

Lister Hill National Center for Biomedical Communications
National Institutes of Health (U.S.)
National Library of Medicine

Keywords and Phrases

CBIR; K-Means Clustering; NHANES; Cervical Spine; Claw; Image Processing; Nearest Neighbor Classification; Osteoarthritis; Osteophyte; Traction; X-Ray

International Standard Serial Number (ISSN)

0895-6111

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2008 Elsevier, All rights reserved.

Publication Date

01 Jan 2008

Link to Full Text

Share

 
COinS