Objectives: Upon completion of this course, students should be able to:

1. Salman Bin Abdulziz University College of Applied Medical SciencesMedical Equipment Technology Department Computer Image Processing (BMTS 492)Dr. Omar Alfarouk

2. Objectives: Upon completion of this course, students should be able to: • Understand the basic concepts of digital image processing. • Perform various image processing techniques such as image filteration, image segmentation, image enhancement, and restoration.. • Use MATLAB for image processing. Course Description. This course deals with digital image processing on computer includes: statistics on the image, the notion of pixel, value representation in gray level images, color images, and operation on pixels for image enhancement. It covers also convolution application for different type of filters on images for noise reduction, enhancement using operation on histograms, linear and non linear filters, image enhancement by histogram equalization, filter based on Fourier space and image restoration.

5. Chapter I: Basics of Image Analysis • Sampling and Quantization • To be suitable for computer processing an image function f(x,y) must be digitized both spatially and Amplitude. • Digitization of the spatial coordinates (x,y) is called image sampling, and amplitude quantization is called gray-level quantization.

6. All of the sampling theorem concepts apply to the sampling of 2D signals or images .

7. In most real-life applications of imaging and image processing, it is not possible to estimate the frequency content of the images.

8. Adequate sampling frequencies need to be established for each type of image or application based upon prior experience and knowledge.

9. (a) 225x250 pixels; (b) 112 x 125 pixels; (c) 56 x 62 pixels; and (d) 28 x 31 pixels. All four images have 256 gray levels at 8 bits per pixel.

10. 8-bits (256 grey levels) image

11. 1-bit (2-grey levels) image

12. A digitized image function can be represented by a matrix F(x,y) ≈

14. Each element of the matrix represent a picture element or a pixel. The digitization process requires, as a common practice: • N = 2n , M = 2k • And the gray levels (the pixel amplitude) G = 2m • The total number of bits required to store a digitized image: • B = NxMxm • If N =M, • B = mN2 • For example a 512x512 image with 256 gray levels requires 2097152 bits or 2Mbytes of space. The resolution of an image depends very much on the number of pixels and the number of gray levels.

15. 1.2 Brightness and Contrast • The higher the amplitude or the intensity I(x,y) of the pixel the brighter the pixel and hence the image looks brighter if a large number of pixels have high intensities, if B=0, identifies an empty image. The image Brightness is defined as average pixel value:

17. And image Contrast is defined as the spread of pixel values about the average, the lower the spread the lower is the contrast:

19. At the pixel level, the contrast is defined as the difference between its value I(x,y) and the average background intensity normalized by the full intensity range:

21. 1.3 Arithmetic Operations • Addition and Subtraction • The most commonly required arithmetic operations for combining two separate images are (pixel-by-pixel) addition and subtraction:

22. Addition : I(x,y) = min[I1(x,y) + I2(x,y); Imax], • Subtraction: I(x,y) = max[I1(x,y) - I2(x,y); Imin],

23. For an 8-bit gray level image Imin = 0 and Imax = 255. The most important thing to be aware is overflow and underflow that leads to image clipping. In addition if sum exceeds Imax will be set to Imax. If the difference falls below 0, it will be set to 0.

24. Image clipping can be avoided if the range of intensity values is rescaled before the images are combined. You need to identify maximal and minimal output intensities (I1 + I2)max and (I1 - I2)min respectively. These values are used as scale factors.

25. Modified Addition I(x,y) = [I1(x,y) + I2(x,y)]x255 (I1 + I2)max • Modified Subtraction I(x,y) = [I1(x,y) - I2(x,y) + |(I1-I2)min|]x255 [255 + |(I1 - I2)min|]

26. Example 1.0 • Add the following 4-pixel images: • Image (1) 90 200 Image (2) 70 100 • 70 0 10 50

27. Solution: By scanning the two images, • (I1 + I2)max = 200+100=300 • Image (1) + Image (2) = (90+70)x255/300 (200+100)x255/300 (70+10)x255/300 ( 0 + 50)x255/300

28. = 139 255 68 43

29. Example 1.1 • Image (1) 90 200 Image (2) 70 100 • 70 0 10 50 Subtract Image (2) from Image (1) ______________________________ • Solution • (I1 - I2)min = - 50 • Image (1) - Image (2) =

30. (90-70+50)x255/(255+50) (200 -100+50)x255/(255+50) (70-10+50)x255/(255+50) (0 - 50 +50)x255/(255+50)

31. = 59 125 92 0

32. Addition has application in noise averaging and subtraction is used in digital angiography. • Division I(x,y) = • [I1(x,y)/((I2(x,y) + 1))]x255 ((I1/(I2+1))max

33. Addition has application in noise averaging and subtraction is used in digital angiography. • Division I(x,y) = • [I1(x,y)/(I2(x,y) + 1)]x255/((I1/(I2+1))max

34. Division is used in flat fielding as in video microscopy when images are recorded with cameras that exhibit nonlinear output characteristics. Boolean combination of images also possible.

35. Addition noise reduction

36. Spinal column subtraction

37. Types of Digital images • Four Basic types of images • Binary. Each pixel is just black or white (see Fig 1.2b), 1-bit per pixel. Such images are very efficient in terms of storage. Text, fingerprints, or architectural plans are examples of binary images. The matrix of binary images contain one and zeros. • Grayscale. Each pixel is a shade of gray from 0 (black) to 255 (white), needs 8-bits representation, see Fig 1.2a. Find application in medicine (x-rays).

38. RGB • True color or red-green-blue RGB. Each pixel is a mixture of three amount of RGB colors. If each component has a range of 0-255, this gives a total of 2563 = 16777216 different possible colours. The total number of bits for each pixel is 24. RGB image consists of a stack of three matrices.

40. RGB Color Model

41. A NERVE CELL

42. IndexedImages • Index color images uses a colormap. The colormap sets colors to the matrix. A colormap is an m-by-3 matrix of real numbers between 0.0 and 1.0. Each row is an RGB vector that defines one color. The kth row of the colormap defines the k-th color.

43. An Indexed Image

44. Image Format • Tagged image file format (TIFF), is particularly general format, because it allows binary, grayscale, RGB, and indexed color images, as well as different amounts of compression. TIFF is thus a good format for transmitting images between different operating systems and environments. TIFF also allows more than one image per file.

45. 1.4 Intensity Histogram • A histogram is a plot showing the number of image pixels that display each of the possible discrete intensity values.

46. The number of pixels is represented by a histogram pin height. If only one bin is occupied then the corresponding image is completely featureless (uniformly white, black, or gray).

47. If all the bins are occupied then the image brightness is well spread or the contrast is high. For examples (see Fig1.0 and Fig1.1)

48. Fig 1.0 (a) An image of a liver tissue biopsy (b) The histogram computed from the image in (a). Note that the larger peak represent the gray background and the smaller (plateau like) represents the small black regions foreground

49. Fig1.1 (a) Gaafer : Three distinct regions, gives three Histogram peaks (b) Gaafer Histogram.

50. 2.0 Image Enhancements in the Spatial Domain • 2.1 Histogram Expansion: Low contrast images (characterized by a histogram with a narrow peak) can result from limited dynamic range in the imaging system. The idea behind histogram expansion is to increase the dynamic range of the gray level. It is a straightforward and conservative linear transformation.