Digital Image Characteristics

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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.

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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.

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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.

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Image Types

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

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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)

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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.

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Digital Functions

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

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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.

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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 its displayed brightness is determined by
  the adjustment of the viewing window.

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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.

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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.

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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.

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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.
• 80005003048,534.

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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 dont need to worry about writing skills as we
  do with humans.

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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.

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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.

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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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 patients body, not the size of
  the displayed image.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Image Compression

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

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Image Compression

• Lossless compression is when there is no loss of
  image quality and is commonly used.
• The compressed with a lower ratio (lt 51) so the
  files are much larger.

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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 101 to 501
  or higher.

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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.

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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.

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End of Lecture

• Material for this lecture came from the Sprawls
  Educational Foundation