Digital Image Characteristics

1. Digital Image Characteristics Thanks to the work of Dr. Perry Sprawls of Emory University and the Sprawls Educational Foundation, this material is available on-line.

2. Image Types • There are two types of images, analog and digital , using in radiology. • Analog images are images that we can see with the eye composed of colors or shades of gray.

3. Image Types • Digital Images are recorded as many numbers. • The picture is divided into a matrix or array of very small picture elements or pixels. • Each pixel has a numerical value.

4. Image Types • The advantage of digital images is that they can be processed in many ways by a computer system.

5. Digital Advantage • Digital images are an important part of modern healthcare. • Function that can be performed with digital images include: • Image reconstruction (CT, MRI, SPECT, PET) • Image reformations ( multi-plane or view)

6. Digital Functions • Wide dynamic range data acquisition( CT, digital radiography & mammography) • Image processing (change contrast and other characteristics) • Fast image storage & retrieval • Fast and high quality image distribution.

7. Digital Functions • Controlled viewing (windowing, zooming, etc.) • Image analysis (measurements, calculations of various parameters, CAD, etc.

8. Analog Image • Analog images are required for human viewing. • Therefore all digital imaging methods must convert the image to an analog form for viewing and display.

9. Digital Image • A digital image is a matrix of many small picture elements or pixels. • Each pixel is represented by a numerical value. • Generally, at the time of viewing, the actual relationship between the pixel numerical value and it’s displayed brightness is determined by the adjustment of the viewing window.

10. Matrix of Pixels • A digital image is represented in the imaging and computer system by numbers in the form of binary digits called bits. • Here we see the general structure of a digital image.

11. Matrix of Pixels • First, it is divided into a matrix of pixels. • Then, each pixel is represented by a series of bits. • Now we can discover the issues that affect the number of bits per pixels or pixel bit depth.

12. Human Number System • Before we go into how computer systems write numbers, lets review how we write numbers. • We can write ten different digits, 0,1,2,3,4,5,6,7,8 & 9. This is probably due to the number of fingers we have.

13. Human Number System • When we write larger number (more than one digit) the position of a digit within the number has a certain value, 1, 10, 100, 1000 etc as shown here. • The value of a number we have written is just the sum of the values represented by each digit position. • 8000=500+30+4=8,534.

14. Computer Numbers • We know that humans can write 10 different digits. • Digital systems and computers can write only two. • They write numbers by filling in spaces in the computer memory or disc. • However there are only two possible values, marked or blank. • This is called binary (meaning two) digits or for short, bits. • We don’t need to worry about writing skills as we do with humans.

15. Writing Numbers in Bits • When digital systems write numbers, they do it as a series like humans but the digit values are different. • Human digits: 1, 10, 100, 1000 etc. • Binary digits: 1, 2, 4, 8, 16, etc. • The values of the number is just the sum of the values of the marked digits as seen here. • It is just that simple.

16. Values Represented by Four Bits • One of the limitations with using binary numbers is the range of value that can be written is limited to a specific number of bits. • With 4 bits, there are 16 possible ways that the 4 bits can be marked.

17. Values Represented by Four Bits • The range of possible values that can be written is increased by using more bits. • As shown by the equation, the range (number of possible values) is the number 2 multiplied by itself, or raised to the power, by the number of bits. • The range of bits is doubled for each additional bit used.

18. Pixel Bit Depth • The pixel depth is the number of bits that have been available in the digital system to represent each pixel in the image. • Here we have 4 bits but that is much to small for producing a digital image.

19. Eight-bit Pixel Depth • When the pixel bit depth is increased to eight bits, a pixel can have 256 different value (brightness levels, shades of gray).

20. The Effects of Bit Depth on the Image • Here we have three images displayed at different bit depth. • The first image has 1 bit depth. There are only two possible values BLACK or WHITE. • The second image with 4 bit depth has 16 different levels of brightness.

21. The Effects of Bit Depth on the Image • The last image with 8 bit depth can display 256 different brightness levels. This is generally adequate for human viewing.

22. Pixel Size and Digital Image Detail • When the image is in digital form, it is actually blurred by the size of the pixel. • This is because all of anatomical detail is blurred together and represented by one number.

23. Pixel Size and Digital Image Detail • The physical size of the pixel, relative to the objects is the amount of blurring added to the image by digital processing. • Here we see that an image with small bits will have less blurring.

24. Factors Affecting Pixel Size and Image Detail • The size of the pixel (and image detail) is determined by the ratio of the actual size and the size of the image matrix. • Image size is the dimensions of the field of view (FOV) within the patient’s body, not the size of the displayed image.

25. Factors Affecting Pixel Size and Image Detail • Matrix size is the number of pixels along the length and width of an image. • This can be the same in both directions, but generally will be different for rectangular images to produce relatively square pixels.

26. The Effects of Matrix size on Pixel Size and Image Detail • Increasing the matrix size, for example from 1024 to 2048 pixels, without changing the field of view, will produce smaller pixels. • This will generally reduce blurring and improve detail.

27. Image Matrix size for different Imaging Modalities • Different matrix sizes are used for different imaging modalities, this is to produce a pixel size that is compatible with the blurring and detail characteristics of each modality • Also with many imaging modalities, the matrix size can be adjusted by the operator to optimize image quality and the imaging procedure.

28. Effect of FOV on Image Detail • When the Field of View is reduced without changing the matrix, the pixels become smaller and the visible detail is improved. • A practical issue is that larger images (chest) require larger matrix than smaller images to have good detail.

29. The Numerical Size of a Digital Image • The numerical size (number of bits) of an image is the product of two factors: • 1. The number of pixels which is found by multiplying pixel length and width of the image. • 2. The bit depth (bits per pixel) This is usually in the range of8-16 bits or 1-2 bytes per pixel.

30. The Numerical Size of a Digital Image • The significance of the number is that the larger images numerically require more memory and disc space and longer time to process and distribute the images.

31. The Numerical Size of a Digital Image • Typical image size by modality: • 128 x 128 Nuclear Medicine • 256 x 256 Magnetic Resonance Imaging • 512 x 512 Computed Radiography • 2048 x 2048 Digital Radiography • 4000 x 5000 Digital Mammography • Bit depth range from 8 to 24.

32. Image Compression • Image compression is the process of reducing the numerical value of digital images. • There are many different mathematical methods used for image compression.

33. Image Compression • The level of compression is the factor by which the numerical value is reduced. It depends upon: • Compression method • Level of compression

34. Image Compression • Lossless compression is when there is no loss of image quality and is commonly used. • The compressed with a lower ratio (< 5:1) so the files are much larger.

35. Image Compression • Lossee compression produces smaller files but there is a loss of image quality. One must be careful using it on diagnostic images. • The compression ratio is much higher 10:1 to 50:1 or higher.

36. Lossee Compression • Extreme care must be taken when using lossee compression. The FDA requires that the images be labeled as Lossee Images with the ratio used. • It should never be used prior to interpretation. May be useful for negative exams for storage.

37. Irreversible Compression • Some compression algorithms are not reversible but are very effective in reducing file size. • Some are lossless and some are lossee. • JPEG can be either depending upon the exam type. Chest exams are more tolerant than bone examinations. • Other irreversible systems include wavelet based methods, advanced wavelet techniques and JPEG2000.

38. End of Lecture Material for this lecture came from the Sprawls Educational Foundation