Mobile Microscopy Group #33 Rui Guo, Yongli Chen, Xiaoyu Qin

Overview  Incorporated LED circuit into QPI(Quantitative Phase Imaging) Design  An migrated implementation of phase extraction program on iOS devices

Objectives  Want to process and capture image on mobile devices  Process different kinds of images and analyze the performance  Incorporate PCB in the microscopy design

Why phase image?  Provide quantitative information about biological specimens(i.e. dry mass)  Phase of a field is much more sensitive to the specimen structure than its amplitude

Why DPM(Diffraction Phase Microscopy)?  Noise in phase due to mechanical vibrations and air fluctuations  Compact configuration that cancels out most mechanisms responsible for noise  acquisition speed is limited only by the speed of the camera employed

System Overview  Hardware: 1. DPM system 2. Microscope 3. LED circuit 4. iPhone 5  Software: 1. Built in image capture session 2. Process multiple images at one time (up to 100 images) 3. Scroll view for user to view the preprocess images or phase image results

Hardware Overview  LED circuit  Provide illumination to the microscope  DPM system  Active filter used in the microscopy  Microscope  Zeiss Z1 observer with adjustable magnification up to 40x  iPhone 5  iOS device

System Overview Block Diagram LED Light Source Microscope Port DPM System CCD & ProjectoriPhone camera Data Collection & Preprocess

Setup

DPM System Block Diagram Microscope Output Image Iris Diffraction Grating 4f plane with Pinhole filter Final Image Output to CCD

DPM Schematics

DPM System (our implementation) Diffraction grating First lens 40mm focal length Pinhole filter Second lens 100mm focal length

Diffraction Grating  Starting from the normal incidence condition, rotate the rotation stage until the diffraction order = −1 propagates straight back to the incident beam.  The diffracted beam goes back to the laser cavity (or close to it). Read this angle on the rotation stage and subtract the offset angle.

Verification on focal length of the lens #1 Method 532 nm laser diffuser Collimated lens iris Testing lens Image plane

Verification on focal length of the lens #2 Method

Pinhole Filter  1 st order wave is filtered down using a small pinhole  0 th order wave is fully passed  Two fields interfere at the final image plane to create the interferogram  We tested pinhole by adjusting its position in x, y, z direction. From the CCD camera, we can see how much light passed through the filter. Cross section of pinhole filter

Verification of DPM system Adjust the DPM system until three lobes show up in the Fourier domain Middle main lobe – DC – zeroth order Upper and lower lobes – 1 st order

Circuit Overview  LED light source utilizing Printed Circuit Board  The circuit provides illumination to the microscope, so that the detector will be able to generate pictures with enough brightness.  First prototype was built with bread board.  Final product was built with printed circuit board.  There are three major parts: Arduino Uno microcontroller, switches, and LEDs.

Circuit schematic switches Arduino Uno LED module

LED module consists of: 1.9 x White LEDs 2.3 x Bipolar Junction Transistors 3.9 x 100Ω Resistors at collectors 4.3 x 1k Ω Resistors at bases 5.1 x Power supply from Arduino 6.1 x Ground from Arduino

Switch module

Arduino Uno microcontroller Arduino Uno microcontroller serves as power supply and ground for the whole circuit. The microcontroller also controls intensity of LEDs through switches, as described in the previous slide.

First prototype

Printed Circuit Board

Verification of light source circuit  We made very careful adjustment to the position of white LED.  The LED needs to sit on the focus of collimated lens.  Through observing the output from CCD camera, we found an optimal position for the LED.

Software Overview  Utilized iOS OpenCV Framework:  Computation efficiency  Quick functions to use(i.e. dft, normalize, magnitude)  Created bridge between C++ and objective C  Image Format Conversion: UIImage (oc) to Mat(c++).  Phase extraction written in c++  Event and outlet design & link written in objective c Basic steps to extract a phase image

Solved challenges and bug fixes  Xcode built in image picker can only select one image.  Solved by writing own image picker which can select and save multiple images into an array  Fourier spectrum is not centered because OpenCV is lack of shifting algorithm  Solved by designing a function which cut matrix into pieces and form a centered spectrum after reorganization.  Supporting different format of images  Created an image conversion function that can support most common 16 bit image format (i.e. jpeg, tiff, png, bmp)

Major obstacles in software  Information lost due to image format (i.e. JPEG vs TIFF)  fftshift algorithm doesn’t work as fast as in MATLAB  Runtime limited by the computation speed of the phone  Maximum images to process because of limitation in memory

Simulation result on iPhone Left: red blood cell Right: phase image

More Simulation Results  Left: hand imposed on a grating  Right: Phase image of hand

Conclusion  We could encapsulate the whole system so that the DPM will have more mobility  All the test results are within the range of the requirement  As a result, we can apply DPM system to other microscopes  Overall design works properly  Successful migration to mobile device

Further Development  Optimize the application, do the video processing instead of image sequence  Try with powerful white LED so that we can ideally build a portable microscope  Take various images of different objects and discover the potential application of the software