Msc computer science department ,digital image proccesing course

1. Digital Image Processing
(COSC 603)
Tessfu G. (PhD)
School of Computing
Department of Computer Science
Dire Dawa Institute of Technology

2. Introduction to Digital Image
Processing

3. 3
• To define the digital image processing topic.
• To give an idea of digital image processing application
areas.
• To give an overview of a typical image processing system.
Objectives

4. 4
• A digital image is a visual representation of something.
• An image, digital image, or still image is a binary representation of
visual information, such as drawings, pictures, graphs, logos, or
individual video frames.
• Digital images can be saved electronically on any storage device.
• Digitally stored visual information that has a similar appearance to some
subject usually a physical object or a person.
• Produced either by capturing or rendering.
Digital Image

5. 5
Digital Image
Moon image captured
by a Camera.
Moon image rendered
using Ms Paint software

6. 6
2-D Image
2-d image
• two-dimensional (2-d)

7. 7
3-D Image
• 3-d images can be viewed as a sequence of slices (2-d images)
Image of the human skull.
This image was acquired with an MRI
and a 3-d rendering of the data.

8. 8
3-D MRI Brain Image
© Dolphinimaging

9. 9
• Is the use of computer algorithms to perform image processing on digital
images
• Subfield of digital signal processing.
Digital Image Processing (DIP)
Image 1 Image 2
DIP
Why do we process images?
• Acquire an image
- Correct aperture and color balance
- Reconstruct image from projections
• Prepare for display or printing
- Adjust image size

10. 10
Why do we process images?
• Facilitate picture storage and transmission
- Efficiently store an image in a digital camera
- Send an image from Mars to Earth.
• Enhance and restore images
- Improve visibility of tumor in a radiograph
- Remove scratches from an old movie
• Extract information from images
- Read car plate number
- Measure tumor volume from medical images.

11. 11
Image Processing and Application Examples
• Color enhancement

12. 12
Image Processing and Application Examples
• Gray-level image enhancement
Degraded image Noise-reduced image

13. 13
Image Processing and Application Examples
• Mugshot retrieval
© MIT media lab

14. 14
Image Processing and Application Examples
• Face Detection
© MIT media lab

15. 15
Image Processing and Application Examples
• Face morphing
© MIT media lab

16. 16
Image Processing and Application Examples
• Special effects
© MIT media lab
Photo Simulated color pencils
Simulated oil painting

17. 17
Image Processing and Application Examples
• Pseudo color enhancement for security screening
Source: Gonzalez+Woods, Fig. 6.24

18. 18
Image Processing and Application Examples
• Fingerprint recognition
FBI’s
Integrated
Automated Fingerprint
Identification System IAFIS

19. 19
Image Processing and Application Examples
• Biometrics: Iris recognition

20. 20
Image Processing and Application Examples
• Optical character recognition (OCR) -translation of scanned images
of handwritten, typewritten or printed text into machine-encoded text.

21. 21
Image Processing and Application Examples
• Handwritten recognition
Online recognition – using iPhone

22. 22
Image Processing and Application Examples
• Speech recognition

23. 23
Image Processing and Application Examples
• COVID-19 detection

24. 24
Image Processing and Application Examples
• Extraction of settlement area from an aerial image
Source: INRIA, Sophia-
Antipolis, France

25. 25
Image Processing and Application Examples
• Medical image segmentation – subdivision of image sites
Different contrasts from MRI scan of
human brain.
Automatically identified sites
of brain parts and scars

26. 26
Image Processing and Application Examples
• For oil/gas exploration - DIP is used to highlight, enhance and
extract geological features imaged within 3d seismic data.
3-d seismic data

27. 27
Image Processing and Application Examples
• There are many more ….

28. 28
Fundamental Steps in Image Processing Systems
© V. Krueger, Aalborg U.

29. 29
Fundamental Steps in Image Processing Systems
© V. Krueger, Aalborg U.
1, 2

30. 30
Image Processing steps example
1. Problem : diagnosis for a multiple sclerosis (MS) patient
2. Image acquisition: MRI Scan

31. 31
Image Processing steps example
3
© V. Krueger, Aalborg U.

32. 32
Image Processing steps example
3. Preprocessing
Acquired Preprocessed

33. 33
Image Processing steps example
© V. Krueger, Aalborg U.
4

34. 34
Image Processing steps example
4. Segmentation
Acquired Segmented MS lesion sites

35. 35
Image Processing steps example
© V. Krueger, Aalborg U.
5
6

36. 36
Image Processing steps example
5. Representation and description
• Label - each disconnected object
6. Recognition and interpretations
• Which objects are lesion scars
• Calculate volumes -for each scar
object
- Compare with earlier scans
• Lesion volume has decreased?
increased?
- Effect of treatment