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www.mediacy.com Introduction to Image Processing and Analysis Starting Soon…
www.mediacy.com Overview Analytical Imaging Process or Workflow What is an Image Image Quality and Other Issues Image Processing Analysis Advanced Techniques
www.mediacy.com Sample Preparation* Acquisition – how do we acquire an image into the computer? Enhancement – how do we make it look better for visualization. How do we process the image to extract information? Identification – which attributes of the image are we interested in? Measurement – what information can we obtain? Report Generation – how can we present this information? Archive – how can we store the information? The Analytical Imaging Process
What is an image? A digital image is a numerical representation of a “picture” in a 2D array – a set of numbers interpreted by the computer which creates a visual representation that is understood by humans. 255, 255, 199 143, 97, 187 32, 12, 3423, 22, 11 244, 198, 179 123, 94, 195 32, 43, 5213, 32, 11 253, 217, 23468, 185, 9713, 12, 2711, 14, 26
www.mediacy.com Pixels are identified by their position in a grid (two-dimensional array), referenced by its row (x), and column (y). Image: Pixel Array Pixel = Picture Element Each pixel is a sample of an original image.
www.mediacy.com Binary Digits (bits) Bitonal 0 = Black 1 = White
www.mediacy.com BIT DEPTH is determined by the number of bits used to define each pixel. The greater the bit depth, the greater the number of tones (grayscale or color) that can be represented. What is bit-depth?
www.mediacy.com Bit Depth How many gray levels between the darkest and brightest areas 8-bit 2 8 = 256 gray values 12-bit 2 12 = 4,096 gray values 16-bit 2 16 = 65,536 gray values How many gray levels between the darkest and brightest areas 8-bit 2 8 = 256 gray values 12-bit 2 12 = 4,096 gray values 16-bit 2 16 = 65,536 gray values
www.mediacy.com Dynamic Range Bit Depth –The higher the bit depth, the more grey levels can be detected –8bit = 256 grey levels –12bit = 4096 grey levels –16bit = 65536 grey levels
www.mediacy.com What Makes a Good Image Nothing can substitute for excellent sample preparation Make full use of the dynamic range of your detector (PMT, CCD etc)* Avoid saturation of detector Properly aligned microscope
www.mediacy.com What Makes a Good Image Uneven Illumination White Balance Same exposure and illumination per experiment Proper microscope alignment
www.mediacy.com Signal to Noise Ratio (S/N) The higher your S/N ratio –the greater the contrast in your image –The more detail you are able to see
www.mediacy.com How to increase S/N? Increase signal Decrease noise Proper camera
www.mediacy.com How to increase S/N? Increase signal –Proper filter selection for fluorescence microscopy –More efficient excitation –Improved signal capture Higher NA objective More sensitive detector and/or cooled camera Increased exposure time –Reduce photobleaching
www.mediacy.com How to increase S/N? Decrease noise –Reduce background fluorescence Non-specific binding autofluorescence –Reduce cross talk –Longer integration time –Averaging removes random noise –Image filtering methods (Gaussian, Median etc) –Reduce system noise
www.mediacy.com 1x1 0.108 m/pixel 2x2 0.216 m/pixel 3x3 0.324 m/pixel 4x4 0.432 m/pixel Same display settings Different contrast and brightness Images courtesy of Claire M. Brown, PhD, McGill University Department of Biochemistry Camera Binning
www.mediacy.com The number of pixels in the image must be sufficient to distinguish features of interest: Resolution
Nyquist Theorem How many times(frequency) must a sample be measured to be sure of the measurement? –Temporal and spatial frequencies are the same –In fixed tissue analysis we deal with Spatial Frequency –In time-domain analysis we deal with Temporal Frequency This is IMPORTANT. –More than 2.3 samples/resel is a waste and is oversampling –less than 2.3 misses useful information Sampling changes with NA, wavelength and magnification 1 sample/resel no separation 2.3 samples/resel 1 sample/resel no separation
Image Processing Why? –Prepares image/features for analysis –Remove or reduce noise –Enhance or reduce image features Visualization or for analysis purposes Important point about image processing. These operations may or may not change the data, you need to be aware of this and what it means to your results.
www.mediacy.com There are basic ways to enhance an image: Modify its intensity index: brightness, contrast, gamma Background correction: flatten, compensate for irregularities Apply a spatial filter or operation: sharpen, low-pass, edge Advanced enhancement Manipulate the image frequencies: Fourier transform Morphological transformations: erode, dilate, both… Image Enhancement
www.mediacy.com Low dynamic range Medium contrast Full dynamic range Good contrast Enhancement: Grey-value Histogram Stretch
www.mediacy.com brightness contrastAll Three lineargamma 0.5gamma 2 Image Intensity Display Intensity Images courtesy of Claire M. Brown, PhD, McGill University Department of Biochemistry Image Enhancement: All Three
www.mediacy.com Background Correction Background correction Image processing method-Flatten Filter Image Collection-Align system, maintain exp time and illumination
www.mediacy.com Commonly used convolution filters: Low-pass: blurs, or smoothes an object Sharpen: enhances all intensity transitions Hi-pass: enhances high frequency information to increase contrast. Median: removes random impulse noise Advanced Filters: Sigma: removes local impulse noise without Large Spectral: Larger kernal size Lo- and Hi-Pass filter, edge and Band Pass Image Enhancement: Spatial Filtering
www.mediacy.com Provides a method for combining two or more images into a single resultant image. The final results will depend on the operation performed. Logical: AND OR NOT NAND XOR Arithmetic: Add Average Subtract Difference Max & Min Arithmetic operators
www.mediacy.com What Image Features to Preserve? Intensity Spatial Bit depth Some of the choices you make now can impact your ability to measure raw data later
www.mediacy.com Calibrated Measurements Manual Automated Both require that the image be calibrated in advance –How many pixels represent a given distance? –How large an intensity change indicates a positive result?
www.mediacy.com Types of Measurements Histogram Line Profile Manual Measurements –Length, area, angle, thickness, count. Automated Measurements –#of objects, roundness, size, % area, etc. Object Tracking –Distance, velocity Edge Detection and Measurement –Distance between features Volume
www.mediacy.com Histogram Used to evaluate the intensity information and/or analyze the image
www.mediacy.com Line Profile/Automated Edge Detection
www.mediacy.com Counting Objects within Objects The ability to define primary objects in one image (e.g. cells nuclei, composites, etc.) and measure objects from another image that reside within these primary objects. Example, how many DNA repair sites are in each nuclei?
www.mediacy.com Volume rendering Real Time Interaction Clipping Surface rendering Volume of Interest Three Dimensional Reconstruction and Analysis
www.mediacy.com Macro Recording A series of mouse clicks can be recorded Simplifies repeated operations. Reproducibility.
www.mediacy.com Credits Simon Watkins – University of Pittsburgh- CBI MDIBL/Bar Harbor - QFM course MBL/Woods Hole – AQLM course MBL/Woods Hole – OMIB course UTHSCSA- Optical Microscopy course Molecular Expressions Web Site-Mike Davidson
www.mediacy.com Thank You For Attending… Introduction to Image Processing Presented by Jeff Knipe For more information, please contact: email@example.com firstname.lastname@example.org www.mediacy.com Sponsored by: Media Cybernetics