Chan S W, Leung K S, Wong W S
G/F, Samuels Building, School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.
Artif Intell Med. 1996 Feb;8(1):67-90. doi: 10.1016/0933-3657(95)00021-6.
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.
分析子宫颈细胞图像中的异常情况，可为通过检测潜在癌细胞、提供及时建议以及后续跟进和治疗机会来降低宫颈癌死亡率和发病率提供依据。然而，细胞病理学通常基于对形态特征的主观解读。必须为其分类制定任意标准。对此类标准的主观解读很可能导致诊断偏差，进而不同的解读人员之间会出现分歧。本文提出了一种用于生物医学图像分析的分割和诊断过程组合的新方法。已开发出一个原型专家系统，为妇科医生提供客观可靠的工具。使用了特殊的图像分析技术并设计了一组知识源。该专家系统采用了一种强大的控制策略，可将特定领域控制知识的数量降至最低。它已被证明在宫颈癌检测中有效。
