Kalman filtering approaches to enhance scanning speed and precision in automated microscopy

Abstract

Fast and accurate diagnosis of illnesses or other health complications is not accessible in many locations around the world. Due to this, illnesses are unnecessarily left untreated. Therefore, AiDx Medical has developed a portable automated diagnostic microscope with re liable and rapid AI-assisted detection specifically for low-resource settings.

Scan speed is a key issue for the popularisation of whole slide imaging systems [1]. To address this issue a recent paper by [29] proposed a Kalman filter-based scanning algorithm. This approach eliminates the necessity for focus map generation prior to scanning and in doing so, reduces the scan time. Importantly, the proposed approach requires no additional hardware, and is more robust to noise.

In this thesis, two modifications to this work are proposed. Firstly, higher order process models are used to generate more precise estimates of the best-in-focus positions. Secondly, a two-dimensional Non-Symmetric Half Plane Kalman filter is developed to incorporate neighbouring state estimates in the prediction – an approach previously thought inapplicable for this purpose [24]. In a simulation, the new scanning algorithms are applied to scan thin smear malaria specimens and compared to state-of-the-art focus map surveying procedures.