Article
Licensed
Unlicensed Requires Authentication

An image-processing strategy to extract important information suitable for a low-size stimulus pattern in a retinal prosthesis

  • ORCID logo EMAIL logo , , , and
Published/Copyright: March 4, 2017
Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 62 Issue 6

Abstract

Background:

A retinal prosthesis is designed to help the blind to obtain some sight. It consists of an external part and an internal part. The external part is made up of a camera, an image processor and an RF transmitter. The internal part is made up of an RF receiver, implant chip and microelectrode.

Methods:

Currently, the number of microelectrodes is in the hundreds, and we do not know the mechanism for using an electrode to stimulate the optic nerve. A simple hypothesis is that the pixels in an image correspond to the electrode. The images captured by the camera should be processed by suitable strategies to correspond to stimulation from the electrode. Thus, it is a question of how to obtain the important information from the image captured in the picture. Here, we use the region of interest (ROI), a useful algorithm for extracting the ROI, to retain the important information, and to remove the redundant information.

Results:

This paper explains the details of the principles and functions of the ROI. Because we are investigating a real-time system, we need a fast processing ROI as a useful algorithm to extract the ROI. Thus, we simplified the ROI algorithm and used it in an outside image-processing digital signal processing (DSP) system of the retinal prosthesis.

Conclusion:

The results show that our image-processing strategies are suitable for a real-time retinal prosthesis and can eliminate redundant information and provide useful information for expression in a low-size image.

Keywords: cut redundant information in image; image processing; low-size image; region of interest; saliency map; useful information express

Acknowledgments

We would like to express our appreciation for the help by J Boyle, who provided us with his test images.

  1. Funding: This work is supported in part by grants from the National Natural Science Foundation of China (NSFC: 81171402, 60772120, 61471349); the Guangdong Innovative Research Team Program (no. 2011S013) of China; the Science and Technological Program for Dongguan’s Higher Education, Science and Research, and Health Care Institutions (grant no. 2011108101001); the Beijing Center for Mathematics and Information Interdisciplinary Sciences, the Comprehensive Strategic Cooperation Project of Guangdong province and the Chinese Academy of Sciences (grant no. 2011B090300079); and a National 863 project (nos. 2015AA043203 and 2014AA020503).

  2. Conflict of interest: The authors declare that they have no competing interests.

  3. Author contributions: YiliChen performed the image-processing studies and participated in algorithm editing and algorithm development on the DSP. Jixiang Fu and Dawei Chu developed the system platform. Rongmao Li participated in image processing. Yaoqin Xie participated in the design of the system and performed the statistical analysis. All authors read and approved the final manuscript.

References

[1] Ahuja AK, Dorn JD, Caspi A, et al. Blind subjects implanted with the Argus II retinal prothesis are able to improve performance in a spatial-motoe task. Br J Ophthalmol 2011; 95: 539–543.10.1136/bjo.2010.179622Search in Google Scholar PubMed PubMed Central

[2] Alaghi A, Li C, Hayes JP. Stochastic circuits for real-time image-processing applications. Proceedings of the 50th annual design automation conference on – DAC ’13, Austin, TX, USA 2013.10.1145/2463209.2488901Search in Google Scholar

[3] Alpha-IMS of Retina Implant AG [Internet]. Retinaimplant.de [cited 12 April 2013]. http://retina-implant.de/en/doctors/technology/default.aspx.Search in Google Scholar

[4] Boyle J, Maeder A, Boles W. “Inherent visual information for low quality image presentation,” in WDIC 2003–2003 APRS Work-shop on Digital Image Computing: Medical applications of image, pp. 51–56, Canberra, ACT, Australia: The Australian-Recognition Society 2003.Search in Google Scholar

[5] Boyle JR, Maeder AJ, Boles WW. Region-of-interest processing for electronic visual prosthesis. J. Electron Imaging 2008; 17: 013002.10.1117/1.2841708Search in Google Scholar

[6] Busskamp V, Picaud S, Sahel JA, Roska B. Optogenetic therapy for retinitis pigmentosa. Gene Ther 2012; 19: 169–175.10.1038/gt.2011.155Search in Google Scholar PubMed

[7] Carpenter R, Reddi B. Neurophysiology: A conceptual approach. London: Hodder Arnold, CRC Press LLC, 2012: 1.10.1201/b13510Search in Google Scholar

[8] daCruz L, Coley BF, Dorn J, et al. The Argus II epiretinal prosthesis system allows letter and word reading and long-term function in patients with profound vision loss. Br J Ophthalmol 2013; 97: 632–636.10.1136/bjophthalmol-2012-301525Search in Google Scholar PubMed PubMed Central

[9] daCruz L, Merlini F, Arsiero M, et al. Subjects blinded by outer retinal dystrophies are able to recognize outlined shapes using the Argus(R) Ii retinal prosthesis system: a comparison with the full shapes recognition task. ARVO Annual Meeting Abstract 2012; 53: 5507.Search in Google Scholar

[10] Dorn JD, Ahuja AK, Caspi A, et al. The detection of motion by blind subjects with the Epiretinal 60-Electrode (Argus II) retinal prosthesis blind subjects and motion detection. JAMA Ophthalmol 2013; 131: 183–189.10.1001/2013.jamaophthalmol.221Search in Google Scholar PubMed PubMed Central

[11] Guo F, Yang Y, Gao Y, Wu CK. Design of visual prothesis image processing system based on SOC. Proc. of SPIE Vol. 9233, International symposium on photonics and optoelectronics 2014, 92331B.10.1117/12.2068658Search in Google Scholar

[12] Humayun HS. Intraocular retinal prosthesis. Trans Am Ophthalmol Soc 2001; 99: 271–300.Search in Google Scholar PubMed

[13] Humayun MS, Dorn JD, daCruz L, et al. Interim results from the international trial of Second Sight’s visual prosthesis. Ophthalmology 2012; 119: 779–788.10.1016/j.ophtha.2011.09.028Search in Google Scholar PubMed PubMed Central

[14] Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Patt. Mach. Intell 1998; 20: 1254–1259.10.1109/34.730558Search in Google Scholar

[15] Kauper K, McGovern C, Sherman S, et al. Two-year intraocular delivery of ciliary neurotrophic factor by encapsulated cell technology implants in patients with chronic retinal degenerative diseases. Invest Ophthalmol Vis Sci 2012; 53: 7484–791.10.1167/iovs.12-9970Search in Google Scholar PubMed

[16] Kelly SK, Shire DB, Chen J, et al. A hermetic wireless subretinal neurostimulator for vision prostheses. IEEE Trans Biomed Eng 2011; 58: 3197–3205.10.1109/TBME.2011.2165713Search in Google Scholar PubMed

[17] Liu W, Fink W, Tarbell M, Sivaprakasam M. Image processing and interface for retinal visual prostheses. IEEE J Solid-State Circuits 2007; 13: 1987–1999.Search in Google Scholar

[18] Liu N, Yu T, Yao JP, Yin Z. Development of vision cortex prosthesis. Practical J Clin Med 2010; 7: 27–29.Search in Google Scholar

[19] Luo YH, da Cruz L. A review and update on the surrent status of retinal protheses (bionic eye). Br Med Bull 2014; 109: 31–44.10.1093/bmb/ldu002Search in Google Scholar

[20] Luo YH, Davagnanam I, daCruz L. MRI brain scans in two patients with the Argus II retinal prosthesis. Ophthalmology 2013; 120: 1711–1711.e8.10.1016/j.ophtha.2013.04.021Search in Google Scholar PubMed

[21] Mao W, Que D, Chen H, Yao M. Research on image processing system for retinal prothesis. International Symposium on Computers & Informatics (ISCI 2015).10.2991/isci-15.2015.109Search in Google Scholar

[22] Nirenberg S, Pandarinath C. Retinal prosthetic strategy with the capacity to restore normal vision. Proc Natl Acad Sci USA 2012; 109: 15012–15017.10.1073/pnas.1207035109Search in Google Scholar

[23] Park SJ, An K-H, Lee M. Saliency map model with adaptive masking based on independent component analysis. Neuro Computing 2002; 49: 417–422.10.1016/S0925-2312(02)00637-9Search in Google Scholar

[24] Parka SJ, An KH, Lee M. Saliency map model with adaptive masking based on independent component analysis. Neuro Computing 2002; 49: 417–422.10.1016/S0925-2312(02)00637-9Search in Google Scholar

[25] Ramsden CM, Powner MB, Carr A-JF, et al. Stem cells in retinal regeneration: past, present and future. Development 2013; 140: 2576–2585.10.1242/dev.092270Search in Google Scholar PubMed PubMed Central

[26] Sakaguchi H, Kamei M, Nishida K, et al. Implantation of a newly developed direct optic nerve electrode device for artificial vision in rabbits. J Artif Organs 2012; 15: 295–300.10.1007/s10047-012-0642-8Search in Google Scholar PubMed

[27] Saliency map source code sourced from iLab, University of Southern California: http://ilab.usc.edu/toolkit/.Search in Google Scholar

[28] Schön C, Biel M, Michalakis S. Gene replacement therapy for retinal CNG channelopathies. Mol Genet Genomics 2013; 288: 459–467.10.1007/s00438-013-0766-4Search in Google Scholar PubMed

[29] Stingl K, Bach M, Bartz-Schmidt KU, et al. Safety and efficacy of subretinal visual implants in humans: methodological aspects. Clin Exp Optom 2013; 96: 4–13.10.1111/j.1444-0938.2012.00816.xSearch in Google Scholar PubMed

[30] Wade N, Swanston M. Visual Perception: An introduction. London: Psychology Press, 2013: 1.10.4324/9780203082263Search in Google Scholar

[31] Wang K, Li XQ, Li XX, Pei WH, Chen HD, Dong JQ. Efficacy and reliability of long-term implantation of multi-channel microelectrode arrays in the optical nerve sheath of rabbit eyes. Vision Res 2011; 51: 1897–1906.10.1016/j.visres.2011.06.019Search in Google Scholar PubMed

[32] Wang L, Mathieson K, Kamins TI, et al. Photovoltaic retinal prosthesis: implant fabrication and performance. J Neural Eng 2012; 9: 046014.10.1088/1741-2560/9/4/046014Search in Google Scholar PubMed PubMed Central

[33] Wang J, Lu Y, Gu L, Zhou C, Chai X. Moving object recognition under simulated prosthetic vision using background-subtraction-based image processing strategies. Infor Sci 2014; 277: 512–524.10.1016/j.ins.2014.02.136Search in Google Scholar

[34] Weiland JD, Faraji B, Greenberg RJ, Humayun MS, Shellock FG. Assessment of MRI issues for the Argus II Retinal Prosthesis. Magn Reson Imaging 2012; 30: 382–389.10.1016/j.mri.2011.12.005Search in Google Scholar PubMed

[35] Weiland JD, Humayun MS, Tanguay AR. Out of darkness: helping the blind see with artificial vision. IEEE Solid-State Circuits Magazine 2012; 4–2: 43–45.10.1109/MSSC.2012.2193089Search in Google Scholar

[36] Wilke R, Gabel VP, Sachs H, et al. Spatial resolution and perception of patterns mediated by a subretinal 16-electrode array in patients blinded by hereditary retinal dystrophies. Invest Ophthalmol Vis Sci 2011; 52: 5995–6003.10.1167/iovs.10-6946Search in Google Scholar PubMed

[37] Wu H, Wang J, Li H, Chai X. Prosthetic vision simulating system and its application based on retinal prosthesis[C]. Information Science, Electronics and Electrical Engineering (ISEEE) 2014 International Conference on, 2014; 1: 425–429.10.1109/InfoSEEE.2014.6948145Search in Google Scholar

[38] Xilinx, System Generator for DSP version 12.1 user’s guide (2010).Search in Google Scholar

Received: 2016-3-6
Accepted: 2016-11-1
Published Online: 2017-3-4
Published in Print: 2017-11-27

©2017 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Breast sentinel lymph node biopsy with imaging towards minimally invasive surgery
  4. Research articles
  5. Tailored interactive sequences for continuous MR-image-guided freehand biopsies of different organs in an open system at 1.0 tesla (T) – Initial experience
  6. In vitro stent assessment by MRI: visibility of lumen and artifacts for 27 modern stents
  7. Computer-assisted system on mandibular canal detection
  8. DCS-SVM: a novel semi-automated method for human brain MR image segmentation
  9. An image-processing strategy to extract important information suitable for a low-size stimulus pattern in a retinal prosthesis
  10. Framework for 2D-3D image fusion of infrared thermography with preoperative MRI
  11. Rapid, automated mosaicking of the human corneal subbasal nerve plexus
  12. Regular research articles
  13. Examination of the reliability of an inertial sensor-based gait analysis system
  14. Response of a physiological controller for ventricular assist devices during acute patho-physiological events: an in vitro study
  15. Effects of the nasal passage on forced oscillation lung function measurements
  16. Design of a mechanism for converting the energy of knee motions by using electroactive polymers
https://doi.org/10.1515/bmt-2016-0049
Keywords for this article
cut redundant information in image; image processing; low-size image; region of interest; saliency map; useful information express

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Breast sentinel lymph node biopsy with imaging towards minimally invasive surgery
  4. Research articles
  5. Tailored interactive sequences for continuous MR-image-guided freehand biopsies of different organs in an open system at 1.0 tesla (T) – Initial experience
  6. In vitro stent assessment by MRI: visibility of lumen and artifacts for 27 modern stents
  7. Computer-assisted system on mandibular canal detection
  8. DCS-SVM: a novel semi-automated method for human brain MR image segmentation
  9. An image-processing strategy to extract important information suitable for a low-size stimulus pattern in a retinal prosthesis
  10. Framework for 2D-3D image fusion of infrared thermography with preoperative MRI
  11. Rapid, automated mosaicking of the human corneal subbasal nerve plexus
  12. Regular research articles
  13. Examination of the reliability of an inertial sensor-based gait analysis system
  14. Response of a physiological controller for ventricular assist devices during acute patho-physiological events: an in vitro study
  15. Effects of the nasal passage on forced oscillation lung function measurements
  16. Design of a mechanism for converting the energy of knee motions by using electroactive polymers
Downloaded on 5.4.2026 from https://www.degruyterbrill.com/document/doi/10.1515/bmt-2016-0049/html
Scroll to top button