Automated histometry in quantitative prostate pathology.

OBJECTIVE

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.

STUDY DESIGN

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.

RESULTS

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.

CONCLUSION

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.