Image Acquisition. Fluorescent Microscope. Imaging Device. Four Types of Images. Benefits of Electronic Detectors. The Imaging Triangle 5.12.

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1 Fluorescent Microscope Image Acquisition Lightsource Optics Detector Fluorescence Microscopy and Quantitative Imaging Image acquired can not be better than image generated by microscope (but it can be worse ) Four Types of Images Imaging Device none of which is the actual specimen Optical image Digital image Continuous image Displayed image Widefield light microscope CCD (Charge-Coupled Device) CMOS (Complementary Metal Oxide Semiconductor) Scanning light microscope PMT (PhotoMultiplier Tubes) Castleman K & Young I in Microscope Image Processing, Academic Press Benefits of Electronic Detectors The Imaging Triangle Sensitivity Single photon detection Spectrum UV to IR sensitivity Dynamic range (16-bit) Speed Visualization of extremely fast and/or slow events Ease of image processing and analysis Scanning confocal (PMT) Sensitivity (signal to noise) Wide-field (CCD) Spatial resolution Acquisition speed

2 CCD Format CCD Format (2) Thin silicon wafer Divided into array of light sensitive regions Capture image information in the form of a localized electrical charge Charge varies with incident light intensity Charge shifted row by row across register and into a serial shift register Charge converted into a digital value Full frame CCD No new image capture while register is read Frame transfer CCD Interline CCD Back illuminated sensitivity Matrix of intensity values PMT Spatial Resolution What is the question? What is the shape of a stripe? How many stripes? What is the animal? Size of animal? Is it an animal? Single point detector Image formed by scanning array of points Zebra image from: Resolution Abbe Distance: Rayleigh Distance: rabbe rairy NA NA Sampling Nyqvist sampling Abbe Airy

3 Resolution vs. Sampling Quantum Efficiency (QE) Rayleigh: Nyqvist: Percentage of photons hitting the detector that results in an electron charge Important in low light experiments Wavelength dependant Andor ixon EMCCD camera Loss of Photons Noise 100 photons per molecule generated 30 photons collected by objective 20% loss in objective and mirror 24photons 20% loss in filters 20 photons QE of camera 90% = 18 photons 60% = 12 photons 30% = 6 photons Photon noise The arrival rate of photons on the CCD has statistical variation (Poisson distribution) Dark noise Electrons generated by the CCD independent of photons (temperature dependent) Read noise Combination of noise arrising from charge to voltage signal and subsequent A/D conversion (evenly applied to entire image) Binning Frame Rate (Temporal Resolution) Combination of multiple adjacent pixels to form one brighter pixel Brighter signal with same exposure time but loss of resolution 4 signal 4 read noise Acquisition time Shutter speed, QE, signal strength Frame readout time Move charge and convert to digital value Lag time Clear detector of residual charge 4 signal 1 read noise

4 Bit Depth Color Camera A/D converter steps Bit depth Steps Full well capacity and range of A/D converter = How many electrons per step Bayer filter 50% green pixels 25% red pixels 25% blue pixels Color information in other pixels must be extrapolated Color Camera (2) EM CCD Loss of sensitivity at all wavelengths but especially at red and IR Electron multiplying register before readout to minimize read noise Good cooling required BiomedicumImaging Unit Automation Exposure Filter / objective change Camera Auto exposure Motorized stage (x-y-z) Cell culture environment Auto focus Exposure from photometric luminance and exposure time: E = L t Acquisition quality dynamic range fully in use Special need in fluorescence imaging: Exposure time must not exceed bleaching level

5 Metadata Widefield Fluorescence In addition to good images you will also need to store the image aquisition data How do you store this data in a way that it is available for image analysis Proprietary file formats usually store all information known (controlled by automation) Proprietary file formats are work well with proprietary software but Metadata Objective used Resolution, magnification Size of image Pixel size Fluorescent filters Automation Exposure time Multichannel (filter change) Live Cell Imaging Metadata Framerate Environmental conditions Temperature, CO 2... Automation Fast camera cycles High framerate Short exposure times Framerate, light toxicity Compensation for drift Autofocus, tracking... Deconvolution Convolution Deconvolution When using a microscope you are not looking at the object, you are looking at an image of the object Deconvolution is reversing the optical distortion If the point spread function (PSF) is known deconvolution should be a matter of computing its complementary function But it won t work deconvolution can be divided into deblurring and image restoration algorithms deblurring algorithms are image enhancement techniques that remove blur image restoration algorithms are iterative 3D algorithms that preserve quantative relationships in image data image restoration = recovery of an original signal from degraded image image restoration improves resolution SVI wiki: Deconvolution software at Biomedicum Autoquant Autodeblur (Blind deconvolution) SVI Huygens

6 Deconvolution Colocalization Effect of PSF on colocalization values y x z Volume Rendering Summary Bitplane Imaris 3 and 4D imaging software Surface rendering / segmentation Size and location of structures Branching etc. of filamentous structures Image vs. Object The digital image is an array of integers obtained by sampling and quantizing the optical image Digitization must be done in a way that does not distort the information needed to solve the problem at hand Metadata is as important as the image itself Takkunen et al 2009 J Cell Mol Med

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