1. Seeram Chapter #3: Digital ImagingCT
Seeram Chapter #3: Digital Imaging

2. Analog vs. Digital Information
continuous information
Can have any of an infinite number of values
Digital
discrete information
Can have a finite number of values limited by
# of digits on display
# of bits used to represent value

3. Analog vs. Digital Images
continuous spatial information
Digital
discrete spatial information

4. Digitizing a Picture Commercial scanner
Renders a photograph into numbers
311, 255, 309, 78, 43, 99, 124,…

5. Analog vs. Digital Images
continuous gray shade information
Digital
Discrete gray shade information

6. Digital Image Formation
Screen Wire Mesh
Clinical Image

7. Digital Image Formation: Sampling
Place mesh over image
Assign each square (pixel) a value based on density
Pixel values form the digital image
194 73 22

8. Digital Image Formation: Sampling
Each pixel assigned a value
Value averages entire pixel
Any spatial variation within a pixel is lost
The larger the pixel, the more variation
194 73 22

9. Digital Image Formation
The finer the mesh (sampling), the more accurate the digital rendering

10. What is this?
12 X 9 Matrix

11. Same object, smaller squares
24 X 18 Matrix

12. Same object, smaller squares
48 X 36 Matrix

13. Same object, smaller squares
96 X 72 Matrix

14. Same object, smaller squares
192 X 144 Matrix

15. The Bit Fundamental unit of computer storage Only 2 allowable values1
Computers do all operations with 0’s & 1’s BUT Computers group bits together

16. Popular Bit Groupings Bit (binary digit) Byte Word Double Word
Smallest binary unit; has value 0 or 1 only
Byte
8 bits
28 = 256 unique values
Word
16 bits
216 = unique values
Double Word
32 binary bits ( )

17. # of values which can be represented by 1 bit
2 unique combinations / values 1 2

18. # of values which can be represented by 2 bits1 2
4 unique combinations / values 3 4

19. # of values which can be represented by 3 bits5 1 6 2 7 3 8 4
8 unique combinations / values

20. Digital Image Bit Depth
bit depth controls # of possible values a pixel can have
increasing bit depth results in
more possible values for a pixel
better contrast resolution 1 2 3 . 8
0, 1
00, 01, 10, 11
000, 001, 010, 011, 100, 101, 110, 111
, ,
2 1 = 2
2 2 = 4
2 3 = 8
2 8 = 256
Bits
Values
# Values

21. # of Possible Values & Contrast Resolution
The more possible values for a pixel, the more gray shades & the better the contrast.
4 grade shades
256 grade shades

22. Digital Image Formation Quantization (A to D Conversion)
Process of assigning a number to a gray shade
Only discrete #’s assigned
can lose information because of discrete # assignment 88 ? 89
The middle pixel attenuates between the other two. What # will the A to D converter assign it?

23. Analog to Digital Converter88 ? 89
Since there are no #’s between 88 & 89 (88.5 not allowed), the A to D converter will assign pixel either a 88 or a 89.
The fact that the center pixel is darker than the left one and lighter than the right one is forever lost.

24. Contrast Resolution
difference in x-ray attenuation required for 2 pixels to be assigned different digital values 88 89

25. Gray Scale the more candidate values for a pixel
the more shades of gray image can be stored in digital image
The less difference between x-ray attenuation required to guarantee different pixel values
See next slide

26. 1 2 3 4 5 6 7
Setting pixel values 1 2 3 4 5 6 7 11 8 10 14 12 9 13

27. Display Limitations 17 = 17 = 65 65 = =
not possible to display all shades of gray simultaneously
window & level controls determine how pixel values are mapped to gray shades
numbers (pixel values) do not change; window & level only change gray shade mapping 17 = 17 =
Change window / level 65 65 = =

28. Presentation of Brightness Levels
Pre-processing
Assignment of values to a pixel
In CT values assigned according to attenuation
Post-processing
Each pixel value assigned a brightness
Dynamic process
Assigned brightness for a particular pixel values can be changeg
Window
Level
Does not affect image data
125 25
311
111
182
222
176
199
192 85 69
133
149
112 77
103
118
139
154
120
145
301
256
223
287
225
178
322
325
299
353
333
300

29. Digital Image Sources CT MRI CR DR Digital Subtraction Angiography
Ultrasound
Nuclear Medicine

30. Why Digital? Required for computer use Perfect image copies
Compression
Reduction of image size in computer

31. Why Digital? Image Manipulation Rotation White/black reversal Zoom
Window/level
Enhancement
Edge enhancement
Smoothing (noise reduction)
Analysis
Image statistics
Pattern recognition

32. Image Processing Techniques
Point Operations
Spatial Frequency Filtering
Geometric Operations
Some Operation
Output / Display Pixel Values
(Gray shades)
Input Pixel Values
Don’t Change
Altered by Operation

33. Point Operations Value of each pixel altered according to some rule
New pixel values assigned on pixel by pixel basis
independent of adjoining pixels
Example: Gray level mapping: window / level
Look-up table (LUT) altered
Maps pixel value to gray shades

34. Histogram Graph showing # of pixels at each gray shade
Altered by point operations
Pixel Value #
Pixel Value #

35. Local Operations
AKA
Area processes
Group processes
Modification of input pixel based upon values of pixels close by

36. Local Operations High frequency image Low frequency image
brightness changes rapidly with distance
Small pixels required
Low frequency image
brightness changes slowly with distance

37. Spatial Frequency Filtering
Can increase or decrease brightness changes with distance
Increasing
Sharpens image
Increases noise
Decreases
Blurs image
Smoothes image
Decreases noise

38. Sharpening Image
Original Image
Sharpened Image
Note Increased Noise

39. Note Decreased Noise & Blurring
Smoothed Image
Smoothed Image
Note Decreased Noise & Blurring
Original Image

40. Geometric Operations Scaling Sizing Rotation Translation Modifies
Orientation of pixels
Spatial position of pixels

41. Image Processing Hardware
Image Memory
Image Processor
Digital to Analog Converter
Host Computer

42. Image Processing Hardware
Image Memory
Temporary storage used while processing / displaying image
Image Processor
Digital to Analog Converter
Host Computer

43. Image Processing Hardware
Image Memory
Image Processor
Computer responsible for processing (arithmetic) done on input digital image
Digital to Analog Converter
Host Computer

44. Image Processing Hardware
Image Memory
Image Processor
Digital to Analog Converter
Converts output pixel values from Look-up table to analog voltage required by monitor
Host Computer
125 25
311
111
182
222
176
199
192 85 69
133
149
112 77
103
118
139
154
120
145
301
256
223
287
225
178
322
325
299
353
333
300

45. Image Processing Hardware
Image Memory
Image Processor
Digital to Analog Converter
Host Computer
Directs above hardware
Holds stored images
Directs archiving