An expert system for the detection of cervical cancer cells using knowledge-based image analyzer.
Analyzing for abnormalities of cell images in the cervix uteri provides a basis for reducing deaths and morbidity from cervical cancer through detection of potentially cancerous cells, provision of prompt advice and opportunities for follow-up and treatments. However, cytopathology is usually based on subjective interpretation of morphological features. Arbitrary criteria have to be devised for their classifications. Subjective interpretations of such criteria are likely to result in diagnostic shifts and consequently disagreement occurs between different interpreters. This article presents a novel approach to the composition of segmentation and diagnosis processes for biomedical image analysis. A prototype expert system has been developed to provide an objective and reliable tool to gynaecologists. Special image analyzing techniques are used and a set of knowledge sources is designed. The expert system employs a robust control strategy which minimizes the amount of domain-specific control knowledge. It has been proved to work effectively in the detection of cervical cancer.
Chan
S W
SW
G/F, Samuels Building, School of Computer Science and Engineering, University of New South Wales, Sydney, Australia. swkchan@cse.unsw.edu.au
Leung
K S
KS
Wong
W S
WS
D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D004867:Equipment Design; D005103:Expert Systems; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis*