An algorithm for microscopic specimen delineation and focus candidate selection

In this paper, we compare four field-of-view (FOV) metrics that, when applied to a low-resolution image of a microscope slide, are capable of both accurately delineating the specimen and selecting a subset of focus candidate FOVs required for construction of high-resolution focus map. The metrics evaluated are: threshold index (TI) that measures image intensity; normalised auto-correlation index (NACI) that measures spatial image similarity; auto-phase correlation index (APCI) that measures image phase diversity; and entropy index (EI) that measures the predictability of image intensities. Experiments are undertaken on a data set of forty slides including PAP stained Thin-prep cervical cytology and breast fine-needle aspiration slides and haematoxylin and eosin (HE) stained histology slides. These slides were scanned on an automated bright-field microscope and chosen to be indicative of a variety pathology specimens, containing artefacts such as excess coverslip glue and ink markers. Results are presented on the performance of each metric for correct ranking/segmentation of foreground (specimen) from background, and subsequently selecting focus candidate FOVs characteristic of the specimen's focal plane(s). The experimental results demonstrate that while NACI, APCI and EI are all effective at specimen delineation, only APCI is capable of effectively selecting superior focus candidates and ignoring artefacts.