Business Systems and Knowledge Modelling Lab

This project was initially sponsored by Auto Scan Ltd – a biomedical company in Melbourne. Our society demands rapid turn over rates of diagnosed samples for various pathological conditions in the medical field. In the majority of cases, the rapidity of specimen analysis determines when a patients medical treatment begins. With our growing population there will be an increased demand for automatic diagnostic equipment to cater for the greater number of specimens being processed and analysed in a cost effective manner. Most automated diagnostic systems for detection of cervical cancer cells (e.g., Lema et al., Cancer, Vol. 84(6), (1998)361) by pap smear, detection of external skin melanoma, detection of prostate cancer, detection of plaques in Alzheimer’s disease are based on use of stained specimens.

The staining method used in the slide preparation process has been the accepted method for many years in the laboratories as it can increase the contrast of cell border and the background. However, it involves three to four hours pre-processing of cell samples. The staining process can add chemical effects to the nature of the cell, introduce changes in the living cells functionality after the stained cell sample has been left for a while. Therefore there is a need for developing a new technology for detection of unstained cells rather than using present staining technology. This project investigates a hybrid architecture of intelligent technologies and statistical and mathematical image processing technologies for improving the accuracy of cell recognition in an unstained human tissue.

Practical Outcome: Grants worth $80,000 received. Prototype has been developed. Number of papers have been published