Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7693
Title: | Object-oriented knowledge-based system for image diagnosis |
Authors: | Chan, Samuel W. K. Prof. LEUNG Kwong Sak Wong, W. S. Felix |
Issue Date: | 1996 |
Source: | Applied Artificial Intelligence, 1996, vol.10 (5), pp. 407 - 438 |
Journal: | Applied Artificial Intelligence |
Abstract: | In this article the design and implementation of a high-level image diagnosis system are described. A knowledge-based system, OOI, has been developed to incorporate the image processing abilities. It employs a robust control strategy in the object-oriented approach that minimizes the amount of domain-specific control knowledge. Knowledge objects are constructed that embed specialized methods or metaknowledge in image processing. They work independently of each other. The highly modular architecture allows the knowledge engineers to modify the knowledge without worrying about any unexpected side effects. Reasoning in OOI proceeds in both top-down and in bottom-up schemes. The system has been tested in medical image analysis. Cancer cell lesion classification is used as an application domain to illustrate how image diagnosis can be implemented in our system. Results attest to the efficacy of this framework in both image recognition and diagnosis. © 1996 Taylor & Francis Group, LLC. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/7693 |
ISSN: | 08839514 |
DOI: | 10.1080/088395196118489 |
Appears in Collections: | Applied Data Science - Publication |
Find@HKSYU Show full item record
SCOPUSTM
Citations
4
checked on Nov 17, 2024
Page view(s)
36
Last Week
1
1
Last month
checked on Nov 21, 2024
Google ScholarTM
Impact Indices
Altmetric
PlumX
Metrics
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.