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Established on the foundation of Canon U.S.A.’s corporate philosophy, Kyosei, in November 2012, Canon entered into collaborative research agreements with Massachusetts General Hospital, and Brigham and Women’s Hospital to develop biomedical optical imaging and medical robotics technologies.

This collaboration lead to the opening of the Healthcare Optics Research Laboratory in Cambridge, Massachusetts in June 2013.

The research laboratory is approximately 13,600 square feet and is the home of skilled employees dedicated to developing biomedical optical imaging and medical robotics technologies with the ultimate goal of bringing medical devices to market for a variety of applications.

The areas that the technology will address include:

  • Image Guided Therapy
  • Miniature Endoscopic Imaging
  • Functional Imaging

Healthcare Optics Research Laboratory, Canon U.S.A. is working in conjunction with its parent company, Canon Inc.

Image guided therapies provide better outcome by improving diagnostic accuracy and targeted treatment for diseased tissue. Research in image guided therapy includes, a robotic device for guidance of percutaneous interventions of abdominal and thoracic cavity, and a combination of targeted agent delivery and image guided platform to improve treatment efficacy. The overall purpose is to develop platform technologies for broader applications. One application, among many others, for our robotic device is image-guided cryotherapy of kidney cancer.

[i] Nobuhiko Hata, Sang-Eun Song, Olutayo Olubiyi, Yasumichi Arimitsu, Kosuke Fujimoto, Takahisa Kato, Kemal Tuncali, Soichiro Tani and Junichi Tokuda, Body-mounted robotic instrument guide for image-guided cryotherapy of renal cancer, Medical Physics. 43, 843 (2016).

Overall objective is to expand the applications of minimally invasive diagnostic and therapeutic endoscopy to areas currently considered unreachable. This project utilizes Canon's strong expertise in innovative imaging and optics solutions and is focused on developing an ultra thin, human hair size, high resolution endoscope that will produce three dimensional images, while enabling direct visualization of 'hard to see' anatomies.

Additionally, a parallel project is focusing on the ability of an endoscope to maneuver through small anatomical spaces by developing robotic multipoint motion. This technology allows to control and navigate, on demand, an endoscope tethered to a console for application in neurosurgery, orthopedics, and laryngoscopyi ii.

[i]  Takahisa Kato, Ichiro Okumura, Hidekazu Kose, Kiyoshi Takagi, Nobuhiko Hata. Tendon-driven continuum robot for neuroendoscopy: validation of extended kinematic mapping for hysteresis operation, International Journal of Computer Assisted Radiology and Surgery, pp 1-14, First online: 17 October 2015

[ii] Takahisa Kato, Ichiro Okumura, Sang-Eun Song, Alexandra J. Golby, and Nobuhiko Hata, Tendon-Driven Continuum Robot for Endoscopic Surgery: Preclinical Development and Validation of a Tension Propagation Model, IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 20, NO. 5, OCTOBER 2015.

Despite advances in imaging technologies, coronary artery disease is still the most common cause of death globally. A number of factors are associated with acute coronary event and understanding its complex relationship with vulnerable plaque could play a key role in managing patient population at risk. Canon efforts are geared towards developing an advanced imaging system and arterial catheter capable of simultaneously obtaining high quality information that enable accurate diagnosis and longitudinal management of disease. Basic principle relies on expanding the scope of imaging from three dimensional structural information to molecular and mechanical properties of coronary plaques. Different methods of understanding both, tissue molecular and mechanical properties are currently being investigated.

Additional research efforts are focused on functional imaging of brain for better understanding of physiological parameters impacted due to an injury or medical treatment such as anesthesia. Technical development includes non-invasive bed side quantification of blood flow and oxygenation to cerebral tissue.


Located in San Jose, CA, the Imaging Systems Research Division is home to a research group whose mission is to contribute to create added value in current Canon products and expand new business and applications by researching and developing systems and algorithms in computational imaging and big data analytics.

The Imaging Systems Research Division is home to a research group whose mission is to contribute to create added value in current Canon products and expand new business and applications by researching and developing systems and algorithms in computational imaging and open world computer vision. Located in Silicon Valley, the Imaging Systems Research Division in San Jose, CA leverages the resources of this high-tech region as it investigates emerging technologies. Valuable work performed here contributes to Canon's strong patent portfolio. The group also engages in collaborative relationships with world renowned local universities.

Data is becoming larger, devices are becoming smarter, with new 3D printers...

How do we leverage Big Data to make cameras smarter?

Open world computer vision is the combination of the state-of-the-art artificial intelligence algorithms with information inferred from databases. This can improve the performance of object identification and classification, which can be applied, for example, to network camera solutions.

open world computer vision



The goal of the computational imaging research is to come with theoretical and experimental methods to explore unconventional ways to combine digital imaging capture and novel image processing techniques in order to understand better the nature of scenes we capture and provide proof-of-concept and prototyping for core technologies that enables improvement of imaging capabilities and performance. This work is centered on illumination-based physical sensing in which research is conducted on technologies that can be used to measure arbitrary shapes and materials in 3-D.

Woman taking a photo of a display

  • A. Lin, F. H. Imai, Efficient spectral imaging based on imaging systems with scene adaptation using tunable color pixels, Proc. of 19th IS&T/SID Color and Imaging Conference, pp. 332-338, 2011

  • F. H. Imai, Computational spectral imaging based on adaptive spectral imaging, Proc. of 4th International Workshop, CCIW 2013, Springer, LNCS 7786, p. 35 ff, 2013

  • S-K. Tin, Spectral reflectance by structured light: a simulation study using OptiX, Presented at GPU Technology Conference, 2013

  • F. H. Imai, Material sensing based on spectral decomposition, Proc. of the 12th Congress of the International Color Association, MCS 2013 Symposium, pp.367-370, 2013

  • J. Ye, F. Imai, High resolution multi-spectral image reconstruction on light field via sparse representation, in the Technical Digest of Imaging System and Applications, Optical Society of America, paper IT3A.4, 2015

  • J. Yu, S. Skaff, L. Peng, F. Imai, Leveraging knowledge-based inference for material classification, Proceedings of ACM Multimedia, pp. 1243-1246, 2015

  • C. Liu, S. Skaff, M. Martinello, Learning discriminative spectral bands for material classification, in Advances in Visual Computing, ISVC 2015, Eds. G. Goos, J. Hartmanis, J. van Leeuwen, Springer, pp. 671-681, 2015

  • S. Skaff, S-K. Tin, M. Martinello, Learning optimal incident illumination using spectral bidirectional reflectance distribution function images for material classification, Journal of Imaging Science and Technology 59, Number 6, pp. 604-5-1-60405-9(9), 2015

  • S-K. Tin, J. Ye, M. Nezamabadi, C. Chen, 3D reconstruction of mirror-type objects using efficient ray coding, in Proceedings of IEEE ICCP , pp. 1-11, 2016

  • E. Levine, M. Martinello, M. Nezamabadi, High-precision multi-view camera calibration using a rotation stage, accepted oral paper to be published in the Proceedings of IEEE ICIP, 2016

  • J. Cai, J. Yu, F. Imai, Q. Tian, Towards temporal adaptive representation for video action recognition, accepted paper to be published in the Proceedings of IEEE ICIP, 2016

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