Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy.

We address the location of regions-of-interest in previously scanned sputum smear slides requiring re-examination in automated microscopy for tuberculosis (TB) detection. We focus on the core component of microscope auto-positioning, which is to find a point of reference, position and orientation, on the slide so that it can be used to automatically bring desired fields to the field-of-view of the microscope. We use virtual slide maps together with geometric hashing to localise a query image, which then acts as the point of reference. The true positive rate achieved by the algorithm was above 88% even for noisy query images captured at slide orientations up to 26°. The image registration error, computed as the average mean square error, was less than 14 pixel² (corresponding to 1.02 μm²). The algorithm is inherently robust to changes in slide orientation and placement and showed high tolerance to illumination changes and robustness to noise.