An image retrieval framework for real-time endoscopic image retargeting

Serial endoscopic examinations of a patient are important for early diagnosis of malignancies in the gastrointestinal tract. However, retargeting for optical biopsy is challenging due to extensive tissue variations between examinations, requiring the method to be tolerant to these changes whilst enabling real-time retargeting.

This work presents an image retrieval framework for inter-examination retargeting. We propose both a novel image descriptor tolerant of long-term tissue changes and a novel descriptor matching method in real time. The descriptor is based on histograms generated from regional intensity comparisons over multiple scales, offering stability over long-term appearance changes at the higher levels, whilst remaining discriminative at the lower levels. The matching method then learns a hashing function using random forests, to compress the string and allow for fast image comparison by a simple Hamming distance metric.

A dataset that contains 13 in vivo gastrointestinal videos was collected from six patients, representing serial examinations of each patient, which includes videos captured with significant time intervals. Precision-recall for retargeting shows that our new descriptor outperforms a number of alternative descriptors, whilst our hashing method outperforms a number of alternative hashing approaches.

We have proposed a novel framework for optical biopsy in serial endoscopic examinations. A new descriptor, combined with a novel hashing method, achieves state-of-the-art retargeting, with validation on in vivo videos from six patients. Real-time performance also allows for practical integration without disturbing the existing clinical workflow.