Multi-sensor super-resolution for hybrid range imaging with application to 3-D endoscopy and open surgery

• Verfasst von: Köhler, Thomas [VerfasserIn]
• Verfasst von: Haase, Sven [VerfasserIn]
• Verfasst von: Bauer, Sebastian [VerfasserIn]
• Verfasst von: Wasza, Jakob [VerfasserIn]
• Verfasst von: Kilgus, Thomas [VerfasserIn]
• Verfasst von: Maier-Hein, Lena [VerfasserIn]
• Verfasst von: Stock, Christian [VerfasserIn]
• Verfasst von: Hornegger, Joachim [VerfasserIn]
• Verfasst von: Feußner, Hubertus [VerfasserIn]
• Titel: Multi-sensor super-resolution for hybrid range imaging with application to 3-D endoscopy and open surgery
• Verf.angabe: Thomas Köhler, Sven Haase, Sebastian Bauer, Jakob Wasza, Thomas Kilgus, Lena Maier-Hein, Christian Stock, Joachim Hornegger, Hubertus Feußner
• E-Jahr: 2015
• Jahr: 3 July 2015
• Umfang: 15 S.
• Fussnoten: Gesehen am 15.07.2020
• Titel Quelle: Enthalten in: Medical image analysis
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1996
• Jahr Quelle: 2015
• Band/Heft Quelle: 24(2015), 1, Seite 220-234
• ISSN Quelle: 1361-8423
• DOI: doi:10.1016/j.media.2015.06.011
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: 3-D endoscopy
• Sach-SW: Hybrid range imaging
• Sach-SW: Open surgery
• Sach-SW: Sensor fusion
• Sach-SW: Super-resolution
• K10plus-PPN: 1724796151

Abstract

In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve data from one modality by taking advantage of additional guidance images of a complementary modality. This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photometric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based on the maximum a-posteriori (MAP) principle and reconstruct high-resolution range data from multiple low-resolution frames and complementary photometric information. Robust motion estimation as required for super-resolution is performed on photometric data to derive displacement fields of subpixel accuracy for the associated range images. For improved reconstruction of depth discontinuities, a novel adaptive regularizer exploiting correlations between both modalities is embedded to MAP estimation. We evaluated our method on synthetic data as well as ex-vivo images in open surgery and endoscopy. The proposed multi-sensor framework improves the peak signal-to-noise ratio by 2 dB and structural similarity by 0.03 on average compared to conventional single-sensor approaches. In ex-vivo experiments on porcine organs, our method achieves substantial improvements in terms of depth discontinuity reconstruction.