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Introduction to Medical Imaging (informatics approach)

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Basics about digital images and corresponding parameters

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Introduction to Medical Imaging (informatics approach)

  1. 1. 2 nd Class Dr.F.Jahedi jahedif@gmail.com http://www.jahedif.com
  2. 2.  Analog images  Digital images
  3. 3.  Type of images that we, as humans, look at.  They include such things as photographs, paintings, TV images, and all of our medical images recorded on film or displayed on various display devices, like computer monitors.  What we see in an analog image is various levels of brightness (or film density) and colors.  It is generally continuous and not broken into many small individual pieces.
  4. 4.  Are recorded as many numbers.  The image is divided into a matrix or array of small picture elements, or pixels.  Each pixel is represented by a numerical value.  The advantage of digital images is that they can be processed, in many ways, by computer systems.
  5. 5. 1. Change 2. Storage 3. Transfer
  6. 6.  Image reconstruction (CT, MRI, SPECT, PET, etc)  Image reformatting (Multi-plane, multi-view reconstructions)  Wide (dynamic) range image data acquisition (CT, digital      radiography, etc) Image processing (to change contrast and other quality characteristics) Fast image storage and retrieval Fast and high-quality image distribution (PACS, Teleradiology) Controlled viewing (windowing, zooming, etc) Image analysis (measurements, calculation of various parameters, computer aided diagnosis, etc)
  7. 7.  Each pixel is represented by a numerical value.  In general, the pixel value is related to the brightness or color  That we will see when the digital image is converted into an analog image for display and viewing.  At the time of viewing, the actual relationship between a pixel numerical value and it's displayed brightness is determined by the adjustments of the window control.
  8. 8. Samplings + Imaginary Sampling Grid Analog Image Continuous Values = Image Matrix Pixel Code Values 0 to 255 Digital Image Discrete Values
  9. 9. Analog Digital AD Conversion For human viewing For computer viewing
  10. 10. •One of the limitations is the range of values that can be written with a specific number of bits (binary digits). •By using four bits: •16 different values because there are 16 ways the four bits can be marked. •The range of possible values is increased by using more bits. •The range (number of possible values) is the number 2 multiplied by itself, or raised to the power, by the number of bits.
  11. 11.  Is the number of bits that have been made available in the digital system to represent each pixel in the image. This is smaller than would be used in any actual medical image because with four bits, a pixel would be limited to having only 16 different values (brightness levels or shades of gray).
  12. 12.  When the pixel bit depth is increased to eight bits, a pixel can then have 256 different values (brightness levels, shades of gray, etc).
  13. 13.  Image resolution describes the detail an image holds  The term resolution is often used as a pixel count in digital imaging  An image that is 2048 pixels in width and 1536 pixels in height has a total of 2048 1536 = 3,145,728 pixels or 3.1 megapixels
  14. 14. Different bit depths and possible brightness levels •1st image A pixel can have only two possible values, BLACK or WHITE. •2nd image, four bits per pixel, is limited to 16 different brightness levels (shades of gray) •3rd image, eight bits per pixel, can display 256 different brightness levels. This is generally adequate for human viewing.
  15. 15.  When an image is in digital form, it is actually blurred by the size of the pixel.  This is because all anatomical detail within an individual pixel is "blurred together" and represented by one number.  The physical size of a pixel, relative to the anatomical objects, is the amount of blurring added to the imaging process by the digitizing of the image.  Here we see that an image with small pixels (less blurring) displays much more detail than an image made up of larger pixels.
  16. 16.  The size of a pixel is determined by the ratio of the actual image size and the size of the image matrix.  Image size dimensions of the field of view (FOV)  Matrix size: number of pixels along the within the patient's length and width of an image. body  This can be the same in both directions,  not the size of a displayed image but generally it will be different for rectangular images to produce relatively square pixels.
  17. 17.  Increasing the matrix size, for example from 1024 to 2048 pixels, without changing the image field of view, will produce smaller pixels.  This will generally reduce blurring and improve image detail.
  18. 18.  Different matrix sizes are used for the different imaging modalities  This is to produce a pixel size that is compatible with  Blurring  Detail characteristics of each modality.  With many modalities, the matrix size can be adjusted by the operator to optimize:  Image quality  Imaging procedure
  19. 19.  1. The number of pixels which is found by multiplying the pixel length and width of the image.  2. The bit depth (bits per pixel). This is usually in the range of 8-16 bits, or 1-2 bytes, per pixel.  The larger the image (numerically),  the more memory and disk storage space is required,  more time for processing and distribution of images is required.
  20. 20.  Image compression is the process of reducing the numerical size of digital images.  There are many different mathematical methods used for image compression.  The level of compression is the factor by which the numerical size is reduced. It depends on the compression method and the selected level of compression.  Lossless compression is when there is no loss of image quality, and is commonly used in many medical applications.  Lossy compression results in some loss of image quality and must be used with care for diagnostic images.

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