An image retrieval framework for real-time endoscopic image retargeting

An image retrieval framework for real-time endoscopic image retargeting Int J CARS (2017) 12:1281–1292 DOI 10.1007/s11548-017-1620-7 ORIGINAL ARTICLE An image retrieval framework for real-time endoscopic image retargeting 1 2 3 Menglong Ye · Edward Johns · Benjamin Walter · 3 1 Alexander Meining · Guang-Zhong Yang Received: 25 January 2017 / Accepted: 23 May 2017 / Published online: 2 June 2017 © The Author(s) 2017. This article is an open access publication Abstract descriptor, combined with a novel hashing method, achieves Purpose Serial endoscopic examinations of a patient are state-of-the-art retargeting, with validation on in vivo videos important for early diagnosis of malignancies in the gas- from six patients. Real-time performance also allows for trointestinal tract. However, retargeting for optical biopsy is practical integration without disturbing the existing clinical challenging due to extensive tissue variations between exam- workflow. inations, requiring the method to be tolerant to these changes whilst enabling real-time retargeting. Keywords Endoscopic navigation · Retargeting · Image Method This work presents an image retrieval framework recognition · Binary codes 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 descrip- Introduction tor is based on histograms generated from regional intensity comparisons International Journal of Computer Assisted Radiology and Surgery Springer Journals

An image retrieval framework for real-time endoscopic image retargeting

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Springer International Publishing
Copyright © 2017 by The Author(s)
Medicine & Public Health; Imaging / Radiology; Surgery; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science, general
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