Hamilton P W, Bartels P H, Montironi R, Anderson N H, Thompson D, Diamond J, Trewin S, Bharucha H
Department of Pathology, Queen's University of Belfast, Northern Ireland, U.K.
Anal Quant Cytol Histol. 1998 Oct;20(5):443-60.
To review progress on the development of machine vision and image understanding in prostate tissue histology and to discuss the problems and opportunities afforded to pathology through the use of these techniques.
A variety of concepts in machine vision are explored, and methodologies are described that have been developed to deal with the complexities of histologic imagery. The theory of human vision and its impact on machine vision are discussed. Software has been specifically developed for the analysis of prostate histology, allowing accurate gland segmentation, basal cell identification and measurement of vascularization within lesions.
Image interpretation can be achieved using knowledge-based image analysis and the application of local object-oriented processing. This successfully allows an automated quantitative analysis of histologic morphology in the diagnosis of prostate intraepithelial neoplasia and invasive prostatic cancer. The use of low-power image scanning, based on textural or n-gram mapping, permits the development of fully automated devices for the rapid detection of tissue abnormalities. High-power, knowledge-guided scene segmentation can be carried out for the quantitative analysis of cellular features and the objective grading of the lesion.
Automated tissue section scanning and image interpretation is now possible and holds much promise in prostate pathology and other diagnostically demanding areas. Issues of standardization still need to be addressed, but the development of such systems will undoubtedly enhance our diagnostic capabilities through the automation of time-consuming procedures and the quantitative evaluation of disease processes.
回顾前列腺组织组织学中机器视觉和图像理解的发展进展，并讨论通过使用这些技术给病理学带来的问题和机遇。
探讨了机器视觉中的各种概念，并描述了为处理组织学图像的复杂性而开发的方法。讨论了人类视觉理论及其对机器视觉的影响。专门开发了用于分析前列腺组织学的软件，可实现准确的腺体分割、基底细胞识别以及病变内血管化的测量。
使用基于知识的图像分析和局部面向对象处理的应用可以实现图像解释。这成功地实现了在前列腺上皮内瘤变和浸润性前列腺癌诊断中对组织学形态的自动定量分析。基于纹理或n元语法映射的低功率图像扫描的使用，允许开发用于快速检测组织异常的全自动设备。可以进行高功率、知识引导的场景分割，以对细胞特征进行定量分析并对病变进行客观分级。
现在可以进行自动组织切片扫描和图像解释，这在前列腺病理学和其他诊断要求较高的领域具有很大的前景。标准化问题仍需解决，但此类系统的开发无疑将通过耗时程序的自动化和疾病过程的定量评估来提高我们的诊断能力。
