Image Processing Basics

Saudi Board of Radiology: Physics Refresher Course Kostas Chantziantoniou, MSc 2 , DABR Head, Imaging Physics Section King Faisal Specialist Hospital & Research Centre Biomedical Physics Department Riyadh, Kingdom of Saudi Arabia Image Processing Basics

Image Processing: Basics

They are many factors that determine the diagnostic usability of a digital image:

exposure techniques

detector quality (technology dependent)

scatter

viewing conditions

quality of readers

number of readers

image processing

Image Processing: Basics

Why do we need image processing?

since the digital image is “invisible” it must be prepared for viewing on one

or more output device (laser printer, monitor, etc)

the digital image can be optimized for the application by enhancing or

altering the appearance of structures within it (based on: body part,

diagnostic task, viewing preferences, etc)

it might be possible to analyze the image in the computer and provide

cues to the radiologists to help detect important/suspicious structures

(e.g.: Computed Aided Diagnosis, CAD)

Image Processing: Transformations

They are three types of image processing (transformation algorithms) used:

image-to-image transformations

image-to-information transformations

information-to-image transformations

Image Processing: Image-to-Image Transformations

Image In  Image Out

enhancement (make image more useful, pleasing)

restoration (compensate for known image degradations to produce

an image that is “closer” to the (aerial) image that came out of the

patient - e.g: deblurring, grid line removal)

geometry (scaling/sizing/zooming, morphing one object into

another, distorting or altering the spatial relationship between

pixels)

They are three types of image-to-image transformations:

point transformation

local transformation

global transformation

Point Transformation (use Look-up Tables to adjust Tonescale or image contrast)

the shape of the LUT depends on the desired “look” of the output image

and the structure of the histogram

Image Processing: Image-to-Image Transformations Image contrast window

Image Processing: Image-to-Image Transformations Image brightness window

Non-linear LUTs can be used as well (but more complex to implement)

What LUT shape should be used?

Local Transformation (Edge Enhancement, Zooming)

Edge Enhancement (Un-sharp Masking Technique)

Creating a blurred image

The pixels within the kernel are averaged to determine the value of the center pixel for the output image Repeat process for all pixels in image

Image Processing: Image-to-Image Transformations Kernel size will have a large effect on the level of smoothing that is performed Sum of all pixel weight factors in kernel must equal 1

Creating a “amplified” difference image

Creating the final edge enhanced output image

Global Transformation (Spatial frequency “Fourier” decomposition):

Image Processing: Image-to-Information Transformations

Image In  Information (Data) Out

image statistics (histograms)

image compression

image analysis (image segmentation, feature extraction, pattern

recognition)

computer-aided detection and diagnosis (CAD)

Image Statistics (Histogram)

the histogram is the fundamental tool for

image analysis and image processing

it histogram is created by examining each

pixel in the digital image and counting the

number of occurrences of each pixel value

(or Code Value)

Low Contrast Image Histograms

low contrast images produce tall

and narrow histograms

histogram covers a short range of

pixels values

High Contrast Image Histograms

high contrast images produce

short and flat (wide) histograms

histogram covers a wide range of

pixels values

NOTE: histograms do not care for the location of the pixels (both high contrast images shown above have the same histogram)

Image Compression

medical images can contain huge amounts of data (CT image: 0.25 MB,

CR chest image: 8 MB, Digital Mammo: 32 MB)

image compression aims to reduce the total number of bits needed to

represent the image without compromising image quality, which in turn:

reduces storage requirements

reduces the time required to transmit images

uses existing network bandwidth more effectively

Image compression is more than:

sampling at a lower rate or throwing away pixels

quantizing each pixel more coarsely or reducing the precision of each pixel

Why can images be compressed?

Redundancy : relationship do exist between pixels in an image based on their location

algorithms can be spatial (statistical), temporal or spectral (wavelet) in nature

Irrelevancy : pixels included in image that do not add to the diagnostic information

Two types of image compression are used:

Lossless (Reversible) Compression

uses statistical redundancy only

compression ratios range from 2:1 to 4:1

decompressed (reconstructed) images is numerically identical to original

Lossy (Irreversible) Compression

uses statistical redundancy and irrelevancy

compression ratios range from 6:1 to 20:1 and more

decompressed image is degraded relative to original

Image Analysis (Segmentation)

Original image Original image with segmentation data

Image Processing: Information-to-Image Transformations

Information (Data) In  Image Out

decompression of compressed image data

reconstruction of image slices from CT or MRI raw data

computer graphics, animations and virtual reality (synthetic objects)

3D Image Reconstruction

Image Synthesis

Image Output (Reconstruction): Basics

Why do we need to reconstruct the image?

the digital image is still a 2D array of numbers (pixels values)

if it is to be viewed by a human it must be converted back to an analog

image on some display device and/or medium (e.g.: CRT monitor,

hardcopy)

so digital image must be reconstructed for output device

Image Output (Reconstruction): What is the problem? Nuclear medicine image (96 x 128, 6 bit) to be printed on a laser printer film (4k x 5k, 12 bit)

The problem is:

how do we match the gray scales (tonescale)?

how do we match the image size?

Image Output (Reconstruction): What is the problem? CR image (2k x 2.5k, 12 bit) to be displayed on a CRT monitor (1.2k x 1k, 8 bit)

Image Output (Reconstruction): Tonescale

Output system tonescale depends on:

image processing applied (output device should not change any post

processing that was done on the image prior to this step)

calibration of output device (very important & can vary with time)

dynamic range of output device

viewing conditions

observer

Output Calibration (needs to be performed frequently)

every output device has a LUT that relates its output pixel values to the

input pixel values that generated them

Laser Printer CRT Monitor

Dynamic Range

every output device has a different dynamic range that must be considered

when selecting or calibrating the device LUT

Dynamic Range = Highest signal value device can produce

Lowest signal value device can produce

Dynamic Range = antilog(3.0) = 1000 therefore dynamic range of film is 1,000:1

Must use LUTs that compensate for differences in dynamic range :

CRT monitors: non-linear

Laser printers: linear or non-linear (to introduce additional contrast)

Viewing Conditions

Image Output (Reconstruction): Output Geometry

Image Scaling Techniques

in order to display images properly on the output device, the image may

have to be scaled by the use of one of the following techniques:

decimation (sub-sampling)

interpolation

Image Output (Reconstruction): Decimation

this technique is required when image matrix size is too big for output

device

method of decimation is determined by degree of reduction (may have

image quality concerns)

Methodology

Imaging Concerns

decimation can be dangerous

high frequency signals can be

removed during sub-sampling and

cause artifacts

proper decimation requires that the

digital image be smoothed (blurred)

first to remove any signal frequencies

that are higher then half of the new

sampling frequency

Image Output (Reconstruction): Interpolation

Why do we need to interpolation?

the digital image is too small for output device and we have to scale it up

problem is that when we scale the image up, we have new pixels that will

require new pixel values, that should make the new image appear

continuous in space and in gray scale, note output devices are analog

devices (e.g.: laser printer, CRT monitor)

three interpolation techniques are often used:

nearest neighbor interpolation (pixel replication)

linear (or bilinear) interpolation

cubic (spline) interpolation (nonlinear interpolation)

What are the effects of interpolation?

NOTE the human eye-brain system is an efficient interpolator After blurring your eyes

What does an interpolator do?

creates enough pixels in the new digital image such that the matrix sent to

the output device produces an image of the right size

generates new pixels with gray values in such a way that when the display

aperture (electron gun: CRT’s or laser spot: laser cameras) marks the

output medium, it creates the impression that the image is continuous in

space and continuous in values

NOTE excessive interpolation can degrade image quality

the interpolator uses the known pixels values to calculate or produce new

pixels anywhere within the image

interpolation adds no new information or detail to the image

Image Output (Reconstruction): Nears Neighbor Interpolation

Methodology

Image Output (Reconstruction): Bi-linear Interpolation

Methodology

Image Output (Reconstruction): Cubic Interpolation

Methodology

all reconstructions of analog signals are approximations

which interpolator to use depends on the application needs:

Nearest neighbor : maintains/inserts hard edges around pixels (good

for text and some images like nuclear medicine)

Linear : smoothing effect, sometimes excessive (good to suppress

high frequency structures or noise), very easy to implement

Cubic : can produce very accurate reconstructions but more complex

and costly to implement

Image Output (Reconstruction): Display Aperture

Output device aperture size does effect image quality and perceived image resolution

Image Output (Reconstruction): Addressability/Resolution

Because output device has 2k x 2.5 pixels it does not mean you can see all of them

Addressability (matrix size)

is the data capacity of the output device characterized by the number of

values that are addressable by the user (a 4k x 5k laser printer has about

4000 x 5000 = 20,000,000 addressable points (pixels) over its usable area

Resolution

the ability to see or measure details in the output

device

more important than addressability since it

determines the usefulness of a given output device

is usually lower than addressability (due to effects of

display aperture)

Image Processing Basics

0 comments

Notes on slide 1

1 Favorite