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1 Introduction to Image Processing and Analysis Starting Soon…

2 Overview Analytical Imaging Process or Workflow What is an Image Image Quality and Other Issues Image Processing Analysis Advanced Techniques

3 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


5 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

6 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.

7 Binary Digits (bits) Bitonal 0 = Black 1 = White

8 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?

9 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

10 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

11 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

12 What Makes a Good Image Uneven Illumination White Balance Same exposure and illumination per experiment Proper microscope alignment

13 What Makes a Good Image

14 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

15 How to increase S/N? Increase signal Decrease noise Proper camera

16 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

17 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

18 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

19 The number of pixels in the image must be sufficient to distinguish features of interest: Resolution


21 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



24 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.

25 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

26 Low dynamic range Medium contrast Full dynamic range Good contrast Enhancement: Grey-value Histogram Stretch

27 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

28 Background Correction Background correction Image processing method-Flatten Filter Image Collection-Align system, maintain exp time and illumination

29 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

30 Examples of filter kernels: -3 -3 -3-3 0 +3 -2 -2 -2 0 0 0-3 0 +3 -2 +9 -2 +3 +3 +3-3 0 +3 -2 -2 -2 horizontalvertical sharpening edge detectedge detectfilter Spatial Filters

31 Examples Image Enhancement: Sharpening

32 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

33 Image Operations

34 Red Green Blue Processing / Enhancement Extract Images

35 DAPI Cy3 FITC Processing / Enhancement Merge Images


37 Data Analysis Considerations What measurements are meaningful? How can I optimize my image capture to improve measurement quality? What image features need to be preserved?

38 What Measurements are Meaningful? Spatial –Length, roundness, xy coordinates Temporal –Velocity, distance traveled, vector Volumetric –Shape change, spatial relationship Intensity –Temporal changes, ratiometric comparisons

39 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

40 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?

41 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

42 Histogram Used to evaluate the intensity information and/or analyze the image

43 Line Profile/Automated Edge Detection

44 Thresholding / Segmentation

45 Measurement of Objects

46 Area Percentage Measurements

47 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?

48 Object Tracking

49 Data Output


51 Extended Depth of Field

52 Depth of Field Extended Depth of Field cont…

53 “Stitching” of Images through Automatic Microscope and Stage control Tiling

54 Colocalization Intensities in Time-Series Fluorescence Measurements

55 Dan Mulvihill Cell Developmental Biology Group University of Kent Raw ImageDeconvolvedThreshold Deconvolution - Analysis

56 Deconvolution - Visualization

57 Volume rendering Real Time Interaction Clipping Surface rendering Volume of Interest Three Dimensional Reconstruction and Analysis

58 Macro Recording A series of mouse clicks can be recorded Simplifies repeated operations. Reproducibility.

59 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

60 Thank You For Attending… Introduction to Image Processing Presented by Jeff Knipe For more information, please contact: Sponsored by: Media Cybernetics

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