There are two types of images - analog and digital. Digital images are represented as a matrix of pixels, with each pixel represented by a numerical value. The structure and characteristics of digital images, such as pixel bit depth, matrix size, and field of view, determine image quality and detail. Digital images allow for processing and analysis that analog images do not.

1. Faculty of princess Aisha Bint Al Hussein
Radiographer department
Al-Hussein Bin Tala University
By : Merna Saleh Alkhresheh ( 420180903002 )
Presented to : Dr. Mohammad Al-zboun
Digital Image Characteristics

2. Outline:
Ø Image type
Ø The Digital Advantage
Ø Analog images
Ø Digital Images
Ø The Structure of a Digital Image
Ø Human Digits
Ø Comparing Human and Computer Digits
Ø Writing Numbers in Bits
Ø Range of Values for Numbers in Binary Form
Ø Pixel Bit Depth
Ø Eight- bit Pixel Depth
Ø The Effect of Bit Depth on the Image
Ø Pixel Size and Digital Image Detail
Ø Factors Affecting Pixel Size and Image Detail
Ø The Effect of Matrix Size on Pixel Size and Image Detail
Ø Image Matrix Size for the Different Imaging Modalities
Ø Effect of Field of View on Digital Image Detail
Ø The Numerical Size of a Digital Image
Ø Image Compression

3. There are two types of images, analog and digital, used in medical imaging.
Analog images are the 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.
Digital images are recorded as many number. 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.
** Image Types

5. Digital images are a necessary element in all modern medical imaging methods. Functions that can be
performed with digital images include:
- 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 (teleradiology)
- Controlled viewing (windowing, zooming, etc)
- Image analysis (measurements, calculation of various parameters, computer aided diagnosis, etc)
** The Digital Advantage

7. Analog images are required for human
viewing.
Therefore, all of the medical imaging
methods that produce and use digital
images, must convert the images to an
analog form for display and viewing.
As humans, we cannot get much
information from just looking at a display of
many, many numbers, that is looking
directly at a digital image.
** Analog images

8. A digital image is a matrix of many small elements, or
pixels.
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.
Generally, at the time of viewing, the actual
relationship between a pixel numerical value and its
displayed brightness is determined by the
adjustments of the window control as discussed in
other modules.
** Digital Images

9. 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.
First, it is divided into a matrix of pixels.
Then, each pixel is represented a series of bits.
We will soon discover the issues that affect the
number of pixels in an image and the number of bits
per pixel, the so-called pixel bit depth.
** The Structure of a Digital Image

10. Before we go into how computer systems write
numbers, let's review how we, as humans, write
numbers. We can write ten different digits,
0,1,2,3,4,5,6,7,8, and 9, . This system probably
developed because we have ten fingers.
When we write larger numbers (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.
In this example, 8000 + 500 + 30 + 4 =8,534.
** Human Digits

11. ** Comparing Human and Computer Digits
We have just reviewed that we as humans can write ten different digits.
Digital imaging systems and computers can only write two different digits.
They write numbers by filling in spaces (in the computer memory, on disk, etc).
However, they can only place a mark in the space (the black dot shown here) or leave the
space blank (the white dot shown here).
Therefore, each digit space can have only two possible values, MARKED or BLANK. For
that reason, these are called binary (meaning two) digits, or for short, bits.
There is an advantage in writing in bits. We do not have to be concerned with the
computer's "handwriting", like we must do sometimes for humans. It is easy and accurate
to just make marks.

13. ** Writing Numbers in Bits
When digital systems write numbers, they do it as a
series of digits (like humans) but the digit positions have
different values.
Remember our human digit positions have values: 1, 10,
100, 1000, etc.
The binary (computer) digit positions have values: 1, 2, 4,
8, 16, etc.
The value of a multiple-digit number is just the sum of the
values of the marked digits as we see here.
It is as simple as that!

14. ** Range of Values for Numbers in Binary
Form
One of the limitations with using binary numbers is the range
of values that can be written with a specific number of bits
(binary digits).
We explore the process here by using four bits. As we see,
four bits can have 16 different values because there are 16
ways the four bits can be marked.
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 the number of bits.
The range of possible values is doubled for each additional
bit used as we will soon see.

15. ** Pixel Bit Depth
The pixel bit depth is the number of bits that
have been made available in the digital system
to represent each pixel in the image.Here we
have an example of using only four bits.
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).
Medical images require more, as we will soon
see.

16. When the pixel bit
depth is increased to
eight bits, a pixel can
then have 256
different values
(brightness levels,
shades of gray, etc).
** Eight- bit
Pixel Depth

17. ** The Effect of Bit Depth on the Image
Here we compare three images, displayed with different bit depths and possible
brightness levels.
The first image is displayed with only one bit per pixel. A pixel can have only two possible
values, BLACK or WHITE.
The second image, with four bits per pixel, is limited to 16 different brightness levels
(shades of gray)
The third image, with eight bits per pixel, can display 256 different brightness levels. This
is generally adequate for human viewing.

19. When an image is in digital form, it is 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
** Pixel Size and Digital Image Detail

21. ** Factors Affecting Pixel Size and
Image Detail
The size of a pixel (and image detail) is determined by the ratio of the actual image 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 a displayed image.
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 it will be different for rectangular images to produce
relatively square pixels.

23. 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
** The Effect of Matrix
Size on Pixel Size and
Image Detail

24. Different matrix sizes are used for the 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 modalities, the matrix size can be adjusted by the operator
to optimize image quality and the imaging procedure.
** Image Matrix Size for the
Different Imaging Modalities

25. ** Effect of Field
of View on Digital
Image Detail
When the field of view (FOV)
is reduced, but not changing
the matrix size, the pixels
become smaller, and the
visibility of detail is
improved.
A practical issue is that larger
images (such as a chest
radiograph) require a larger
matrix (more pixels) than a
smaller image in order to
have good detail

26. 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 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 significance of numerical size is that the larger the image (numerically), the more
memory and disk storage space is required, and more time for processing and distribution
of images is required.
** The Numerical Size of a Digital Image

28. 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.
Lessee compression results in some loss of image quality and must be used with
care for diagnostic images
** Image Compression