| Time and space results of dynamic texture feature extraction in MR and CT image analysis. | |
| | |
MedLine Citation:
|
PMID: 10719513 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
|
Texture feature extraction is a fundamental part of texture image analysis. Therefore, the reduction of its computational time and storage requirements should be an aim of continuous research. The Spatial Grey Level Dependence Method (SGLDM) is one of the most important statistical texture description methods, especially in medical image analysis. Co-occurrence matrices are employed for the implementation of this method; however, they are inefficient in terms of computational time and memory space, due to their dependency on the number of gray levels (gray-level range) in the entire image. Since texture is usually measured in a small image region, a large amount of memory is wasted while the computational time of the texture feature extraction operations is unnecessarily raised. Their inefficiency puts up barriers to the wider utilization of SGLDM in a real application environment, such as a clinical environment. In this paper, the memory space and time efficiency of a dynamic approach to texture feature extraction in SGLDM is investigated through a pilot application in the analysis of magnetic resonance (MR) and computed tomography (CT) images. |
| | |
Authors:
|
A E Svolos; A Todd-Pokropek |
Related Documents
:
|
9664593 - Third ventriculostomy, phase-contrast cine mri and endoscopic techniques. 6792653 - Dynamic sequential scanning with table incrementation. 2681373 - Intraventricular hemorrhage. |
Publication Detail:
|
Type: Journal Article |
Journal Detail:
|
Title: IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society Volume: 2 ISSN: 1089-7771 ISO Abbreviation: IEEE Trans Inf Technol Biomed Publication Date: 1998 Jun |
Date Detail:
|
Created Date: 2000-03-31 Completed Date: 2000-03-31 Revised Date: 2000-12-18 |
Medline Journal Info:
|
Nlm Unique ID: 9712259 Medline TA: IEEE Trans Inf Technol Biomed Country: UNITED STATES |
Other Details:
|
Languages: eng Pagination: 48-54 Citation Subset: IM |
Affiliation:
|
Department of Medical Physics, University College London, U.K. svolos@diogenis.ceid.upatras.gr |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
Image Processing, Computer-Assisted Magnetic Resonance Imaging* Tomography, X-Ray Computed* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: PACS/information systems interoperability using Enterprise Communication Framework.
Next Document: A VRML-based anatomical visualization tool for medical education.