OLYMPUS (Nesher) Matlab Coder user story

1. OLYMPUS (Nesher) Matlab Coder user story By: Nikolay Skarbnik.

2. Introduction • About Olympus Medical • Why Matlab Coder? • Research to product: the way it usually works • Research to product: the way we would like it to be • Olympus Nesher user story • Demo to product plan • Project I- video enhancement • The issue • Project II- video denoising • Conclusions Agenda

3. Olympus is a Japan-based word leading manufacturer of optics and reprography products since1919. • Olympus develops innovative capabilities in medical technology, focusing on early detection of diseases and minimally invasive ways to treat them. • Olympus delivers the diagnostic and therapeutic technologies: endoscopes, laparoscopes, video and imaging systems, electro-surgical devices, electronic medical records and accessories. • About Olympus Medical

4. Olympus Surgical Technologies America’s Israel facility, serves as Global Research Center for Olympus Medical. • Founded in 1999, this facility originally developed and manufactured miniature distal tip video endoscopes. • The site employs ~40 employees specializing in the research of imaging algorithms and new concepts for surgical tools. • About Olympus Medical Nesher

5. To make things: • better! • faster! • cheaper! • Did it work? • You will have to wait 13 minutes to find out… • Why did we purchase the Matlab Coder?

6. Research to product: the way it usually works (1) • Algorithms Eng : • Research (Signal/Image processing) • Algorithms selection • Algorithms analysis • Proof of concept/ simulation • Algorithms implementation (inMatlab) • Software Eng : • Hardware oriented implementation • OOP • Real Time • Implementation in: C, C++, DSP, C#, iOS, Java etc… Usually verbal /email explanation. SOW- well documented algorithm, if lucky. State flow charts. Rarely so… Software Eng. /programmer Algorithms Eng. My experience Issues- questions, complaints, misunderstandings (up to verbal violence )…

7. Research to product: the way it usually works (2) • An algorithms eng. working Vs. programmer. • The pros: • Good software quality. • Parallel software and algorithms development • The cons: • Matlab code hardy resembles the C code. • Updates and bug fixes are a mess. Proper documentation is science fiction. • Ping-pong and inter-personal issues occur. • Company must have algorithms team and software team.

8. Research to product: the way it usually works- alternatives • An algorithms eng. With good programming skills. • The pros: • No communication or inter-personal issues. • The cons: • Such a silver bullet is rare & usually costly. • Matlab code still differs from the C code. • Bug fixes and features rarely propagate to the Matlab code. • Product development rate naturally decreases. • Well, forget about any documentation- all is in the genius head.

9. Research to product: the way it should work • An algorithms eng. equipped with good code generator (Coder). • The pros: • Programmers time is saved. • Easy & fast update (bug fix, new features etc.. ). • Almost 100% demo ↔ product compatibility. • The cons: • The Matlab code should be strict & Coder compatible (both good & bad). • Demands form Coder: • Should be cost effective (it is not free to say the least). • The Coder should be really good: • Efficient code • Readable code

10. Matlab Coder work flow defined in Olympus Nesher Environment MEX Regular Matlab code Coder ready Matlab code Matlab Coder MEX C Visual Studio VS compilation & results C code + Open-CV Bug fixes Product compilation & results Texas instrument Adapt C code for TI Bug & HW demand fixes

11. Algorithm in a nut-shell • Frames decomposition • Frame image data is divided to several sub feature space images. • Relevant feature elements are extracted from each sub-image • Frames fusion • Multiple sub-images extracted feature are analyzed. • Features data fused to an image where hardly visible details of interest become notable. Product requirements: • VGA image: 1024x768 • Real time: ~30 [fps] • Quick development ~2 month, with part time DSP eng. • Project I- video signal enhancement

12. Result • Working product: • Quick demo-to-product development time (~1 month). • Row-wise processing issue took ~30% of the time. • Initial product result available promptly (~2 weeks). • Fully aligned with the demo (validation & verification ). • Software Eng. Time: ~2 hours per release (mostly technical work). • Additional HW related optimizations (multi core, optimized functions etc…) coded manually. • Project I- video signal enhancement

13. Project I- image features enhancement The issue

14. Result • Coder based development was abandoned due to the issue pushing project time limits. • Working product via regular development path: • Slow (4 work month) demo-to-product development. • Fine tuning (sub-image operations) required several weeks. • Matlab code has no direct connection to C code. • Product results were available only at project end. • Worries & gray hair whether it will work remained till the end. • Project II- “BM3D like” video denoising

15. We have found Matlab Coder to fit our needs. • It’s utilization is not plug & play. Having invested ~2 weeks in product learning we were ready to work. • We are confident our experience will further improve, as we will become more proficient. • We hope the product will be further improved: • More supported functions. • Code readability (variable names etc…). • Olympus future product algorithms development will rely on Matlab Coder utilization. • Conclusions

16. Questions? • Fin