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An imaging flow cytometer is described in this paper. It possesses the high cell processing rate of a flow cytometer and the high image resolution of an automated microscope. It is a flow cytometer to which has been added a cell detector and velocity measuring circuit and a microscope objective lens that focuses cell illuminated by a laser onto a two-dimensional charge-coupled device (CCD) array. Optical sensing and imaging are carried out in a water filled chamber to reduce optical abberration. The time-delay and integration (TDI) technique is utilized for image acquisition. Image processing and classification is to be carried out in real time by a n-channel, metal-oxide-semicoductor (nMOS) parallel processor array. This imaging flow cytometer can acquires image at a flow rate of 0.5 m/s and in the future will be capable of analysing and sorting cells at high speed on the basis of simultaneous morphology as well as cytochemistry and immunology.

The optic nerve head (optic disc) plays an important role in the diagnosis of retinal diseases. Automatic localization and segmentation of the optic disc is critical towards a good computer-aided diagnosis (CAD) system. In this paper, we propose a method that combines edge detection, the Circular Hough Transform and a statistical deformable model to detect the optic disc from retinal fundus images. The algorithm was evaluated against a data set of 325 digital color fundus images, which includes both normal images and images with various pathologies. The result shows that the average error in area overlap is 11.3% and the average absolute area error is 10.8%, which outperforms existing methods. The result indicates a high correlation with ground truth segmentation and thus demonstrates a good potential for this system to be integrated with other retinal CAD systems.