Online volume rendering of incrementally accumulated LSCEM images for superficial oral cancer detection.

Laser scanning confocal endomicroscope (LSCEM) has emerged as an imaging modality which provides non-invasive, in vivo imaging of biological tissue on a microscopic scale. Scientific visualizations for LSCEM datasets captured by current imaging systems require these datasets to be fully acquired and brought to a separate rendering machine. To extend the features and capabilities of this modality, we propose a system which is capable of performing realtime visualization of LSCEM datasets. Using field-programmable gate arrays, our system performs three tasks in parallel: (1) automated control of dataset acquisition; (2) imaging-rendering system synchronization; and (3) realtime volume rendering of dynamic datasets. Through fusion of LSCEM imaging and volume rendering processes, acquired datasets can be visualized in realtime to provide an immediate perception of the image quality and biological conditions of the subject, further assisting in realtime cancer diagnosis. Subsequently, the imaging procedure can be improved for more accurate diagnosis and reduce the need for repeating the process due to unsatisfactory datasets.