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Image Processing Basics
This document summarizes key concepts in digital image processing, including: 1) Image processing transforms digital images for viewing or analysis and includes image-to-image, image-to-information, and information-to-image transformations. 2) Image-to-image transformations like adjustments to tonescale, contrast, and geometry are used to enhance or alter digital images for output or diagnosis. 3) Image-to-information transformations extract data from images through techniques like histograms, compression, and segmentation for analysis. 4) Information-to-image transformations are needed to reconstruct images for output through techniques like decompression and scaling.
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Image Processing Basics
- 1. Saudi Board ofRadiology: Physics Refresher Course Kostas Chantziantoniou, MSc 2 , DABR Head, Imaging Physics Section King Faisal Specialist Hospital & Research Centre Biomedical Physics Department Riyadh, Kingdom of Saudi Arabia Image Processing Basics
- 2. Image Processing: BasicsThey are many factors that determine the diagnostic usability of a digital image: exposure techniques detector quality (technology dependent) scatter viewing conditions quality of readers number of readers image processing
- 3. Image Processing: BasicsWhy do we need image processing? since the digital image is “invisible” it must be prepared for viewing on one or more output device (laser printer, monitor, etc) the digital image can be optimized for the application by enhancing or altering the appearance of structures within it (based on: body part, diagnostic task, viewing preferences, etc) it might be possible to analyze the image in the computer and provide cues to the radiologists to help detect important/suspicious structures (e.g.: Computed Aided Diagnosis, CAD)
- 4. Image Processing: TransformationsThey are three types of image processing (transformation algorithms) used: image-to-image transformations image-to-information transformations information-to-image transformations
- 5. Image Processing: Image-to-ImageTransformations Image In Image Out enhancement (make image more useful, pleasing) restoration (compensate for known image degradations to produce an image that is “closer” to the (aerial) image that came out of the patient - e.g: deblurring, grid line removal) geometry (scaling/sizing/zooming, morphing one object into another, distorting or altering the spatial relationship between pixels)
- 6. Image Processing: Image-to-ImageTransformations They are three types of image-to-image transformations: point transformation local transformation global transformation
- 7. Image Processing: Image-to-ImageTransformations Point Transformation (use Look-up Tables to adjust Tonescale or image contrast) the shape of the LUT depends on the desired “look” of the output image and the structure of the histogram
- 8. Image Processing: Image-to-ImageTransformations
- 9. Image Processing: Image-to-ImageTransformations
- 10. Image Processing: Image-to-ImageTransformations
- 11. Image Processing: Image-to-ImageTransformations
- 12. Image Processing: Image-to-ImageTransformations Image contrast window
- 13. Image Processing: Image-to-ImageTransformations Image brightness window
- 14. Image Processing: Image-to-ImageTransformations Non-linear LUTs can be used as well (but more complex to implement)
- 15. Image Processing: Image-to-ImageTransformations What LUT shape should be used?
- 16. Image Processing: Image-to-ImageTransformations Local Transformation (Edge Enhancement, Zooming)
- 17. Image Processing: Image-to-ImageTransformations Edge Enhancement (Un-sharp Masking Technique)
- 18. Image Processing: Image-to-ImageTransformations Creating a blurred image The pixels within the kernel are averaged to determine the value of the center pixel for the output image Repeat process for all pixels in image
- 19. Image Processing: Image-to-ImageTransformations Kernel size will have a large effect on the level of smoothing that is performed Sum of all pixel weight factors in kernel must equal 1
- 20. Image Processing: Image-to-ImageTransformations
- 21. Image Processing: Image-to-ImageTransformations Creating a “amplified” difference image
- 22. Image Processing: Image-to-ImageTransformations Creating the final edge enhanced output image
- 23. Image Processing: Image-to-ImageTransformations Global Transformation (Spatial frequency “Fourier” decomposition):
- 24. Image Processing: Image-to-ImageTransformations
- 25. Image Processing: Image-to-InformationTransformations Image In Information (Data) Out image statistics (histograms) image compression image analysis (image segmentation, feature extraction, pattern recognition) computer-aided detection and diagnosis (CAD)
- 26. Image Processing: Image-to-InformationTransformations Image Statistics (Histogram) the histogram is the fundamental tool for image analysis and image processing it histogram is created by examining each pixel in the digital image and counting the number of occurrences of each pixel value (or Code Value)
- 27. Image Processing: Image-to-InformationTransformations
- 28. Image Processing: Image-to-InformationTransformations Low Contrast Image Histograms low contrast images produce tall and narrow histograms histogram covers a short range of pixels values High Contrast Image Histograms high contrast images produce short and flat (wide) histograms histogram covers a wide range of pixels values NOTE: histograms do not care for the location of the pixels (both high contrast images shown above have the same histogram)
- 29. Image Processing: Image-to-InformationTransformations
- 30. Image Processing: Image-to-InformationTransformations Image Compression medical images can contain huge amounts of data (CT image: 0.25 MB, CR chest image: 8 MB, Digital Mammo: 32 MB) image compression aims to reduce the total number of bits needed to represent the image without compromising image quality, which in turn: reduces storage requirements reduces the time required to transmit images uses existing network bandwidth more effectively Image compression is more than: sampling at a lower rate or throwing away pixels quantizing each pixel more coarsely or reducing the precision of each pixel
- 31. Image Processing: Image-to-InformationTransformations Why can images be compressed? Redundancy : relationship do exist between pixels in an image based on their location algorithms can be spatial (statistical), temporal or spectral (wavelet) in nature Irrelevancy : pixels included in image that do not add to the diagnostic information
- 32. Image Processing: Image-to-InformationTransformations Two types of image compression are used: Lossless (Reversible) Compression uses statistical redundancy only compression ratios range from 2:1 to 4:1 decompressed (reconstructed) images is numerically identical to original Lossy (Irreversible) Compression uses statistical redundancy and irrelevancy compression ratios range from 6:1 to 20:1 and more decompressed image is degraded relative to original
- 33. Image Processing: Image-to-InformationTransformations Image Analysis (Segmentation) Original image Original image with segmentation data
- 34. Image Processing: Information-to-ImageTransformations Information (Data) In Image Out decompression of compressed image data reconstruction of image slices from CT or MRI raw data computer graphics, animations and virtual reality (synthetic objects)
- 35. Image Processing: Information-to-ImageTransformations 3D Image Reconstruction
- 36. Image Processing: Information-to-ImageTransformations Image Synthesis
- 37. Image Output (Reconstruction):Basics Why do we need to reconstruct the image? the digital image is still a 2D array of numbers (pixels values) if it is to be viewed by a human it must be converted back to an analog image on some display device and/or medium (e.g.: CRT monitor, hardcopy) so digital image must be reconstructed for output device
- 38. Image Output (Reconstruction):What is the problem? Nuclear medicine image (96 x 128, 6 bit) to be printed on a laser printer film (4k x 5k, 12 bit) The problem is: how do we match the gray scales (tonescale)? how do we match the image size?
- 39. Image Output (Reconstruction):What is the problem? CR image (2k x 2.5k, 12 bit) to be displayed on a CRT monitor (1.2k x 1k, 8 bit)
- 40. Image Output (Reconstruction):Tonescale Output system tonescale depends on: image processing applied (output device should not change any post processing that was done on the image prior to this step) calibration of output device (very important & can vary with time) dynamic range of output device viewing conditions observer
- 41. Image Output (Reconstruction):Tonescale Output Calibration (needs to be performed frequently) every output device has a LUT that relates its output pixel values to the input pixel values that generated them Laser Printer CRT Monitor
- 42. Image Output (Reconstruction):Tonescale Dynamic Range every output device has a different dynamic range that must be considered when selecting or calibrating the device LUT Dynamic Range = Highest signal value device can produce Lowest signal value device can produce Dynamic Range = antilog(3.0) = 1000 therefore dynamic range of film is 1,000:1
- 43. Image Output (Reconstruction):Tonescale Must use LUTs that compensate for differences in dynamic range : CRT monitors: non-linear Laser printers: linear or non-linear (to introduce additional contrast)
- 44. Image Output (Reconstruction):Tonescale Viewing Conditions
- 45. Image Output (Reconstruction):Output Geometry Image Scaling Techniques in order to display images properly on the output device, the image may have to be scaled by the use of one of the following techniques: decimation (sub-sampling) interpolation
- 46. Image Output (Reconstruction):Decimation this technique is required when image matrix size is too big for output device method of decimation is determined by degree of reduction (may have image quality concerns)
- 47. Image Output (Reconstruction):Decimation Methodology
- 48. Image Output (Reconstruction):Decimation Imaging Concerns decimation can be dangerous high frequency signals can be removed during sub-sampling and cause artifacts proper decimation requires that the digital image be smoothed (blurred) first to remove any signal frequencies that are higher then half of the new sampling frequency
- 49. Image Output (Reconstruction):Decimation
- 50. Image Output (Reconstruction):Interpolation Why do we need to interpolation? the digital image is too small for output device and we have to scale it up problem is that when we scale the image up, we have new pixels that will require new pixel values, that should make the new image appear continuous in space and in gray scale, note output devices are analog devices (e.g.: laser printer, CRT monitor) three interpolation techniques are often used: nearest neighbor interpolation (pixel replication) linear (or bilinear) interpolation cubic (spline) interpolation (nonlinear interpolation)
- 51. Image Output (Reconstruction):Interpolation What are the effects of interpolation? NOTE the human eye-brain system is an efficient interpolator After blurring your eyes
- 52. Image Output (Reconstruction):Interpolation What does an interpolator do? creates enough pixels in the new digital image such that the matrix sent to the output device produces an image of the right size generates new pixels with gray values in such a way that when the display aperture (electron gun: CRT’s or laser spot: laser cameras) marks the output medium, it creates the impression that the image is continuous in space and continuous in values NOTE excessive interpolation can degrade image quality
- 53. Image Output (Reconstruction):Interpolation the interpolator uses the known pixels values to calculate or produce new pixels anywhere within the image interpolation adds no new information or detail to the image
- 54. Image Output (Reconstruction):Nears Neighbor Interpolation Methodology
- 55. Image Output (Reconstruction):Bi-linear Interpolation Methodology
- 56. Image Output (Reconstruction):Cubic Interpolation Methodology
- 57. Image Output (Reconstruction):Interpolation all reconstructions of analog signals are approximations which interpolator to use depends on the application needs: Nearest neighbor : maintains/inserts hard edges around pixels (good for text and some images like nuclear medicine) Linear : smoothing effect, sometimes excessive (good to suppress high frequency structures or noise), very easy to implement Cubic : can produce very accurate reconstructions but more complex and costly to implement
- 58. Image Output (Reconstruction):Display Aperture Output device aperture size does effect image quality and perceived image resolution
- 59. Image Output (Reconstruction):Addressability/Resolution Because output device has 2k x 2.5 pixels it does not mean you can see all of them Addressability (matrix size) is the data capacity of the output device characterized by the number of values that are addressable by the user (a 4k x 5k laser printer has about 4000 x 5000 = 20,000,000 addressable points (pixels) over its usable area Resolution the ability to see or measure details in the output device more important than addressability since it determines the usefulness of a given output device is usually lower than addressability (due to effects of display aperture)