  For early diagnosis of malignancies in the gastrointestinal tract,
surveillance endoscopy is increasingly used to monitor abnormal tissue changes
in serial examinations of the same patient. Despite successes with optical
biopsy for in vivo and in situ tissue characterisation, biopsy retargeting for
serial examinations is challenging because tissue may change in appearance
between examinations. In this paper, we propose an inter-examination
retargeting framework for optical biopsy, based on an image descriptor designed
for matching between endoscopic scenes over significant time intervals. Each
scene is described by a hierarchy of regional intensity comparisons at various
scales, offering tolerance to long-term change in tissue appearance whilst
remaining discriminative. Binary coding is then used to compress the descriptor
via a novel random forests approach, providing fast comparisons in Hamming
space and real-time retargeting. Extensive validation conducted on 13 in vivo
gastrointestinal videos, collected from six patients, show that our approach
outperforms state-of-the-art methods.
