Image manipulation

Published on 12/06/2015 by admin

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Last modified 12/06/2015

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7 Image manipulation

Aim of Image Manipulation

To produce an excellent image to maximise diagnostic accuracy

Terminology

Analogue	Represents a quantity changing in steps which are continuous, i.e. a sine wave
Brightness	The intensity values of the individual pixels in an image, the lower the brightness the darker the image
Compression	The reduction in size (in bytes) of an image to save storage space
Contrast	The density difference between two adjacent areas on the image
Digital	An image comprised of discrete areas or pixels
Edge Enhancement	The highlighting of a straight line or edge of an object to visually increase the sharpness of the image
Fourier Transform	A method of mathematically changing data, e.g. changing spatial data to frequency data
Frequency Data	The number of times a specific value occurs in an image
Heuristic	When an image is automatically improved because the program has changed due to a previous imaging experience
Hough Transform	A method of highlighting areas of a specific shape within an image
Noise	Anything that may detract from the image
Resolution (Sharpness)	The size of the smallest object or distance between two objects that must exist before the imaging system will record that object or objects as separate entities.
Segmentation	Selection of an area of interest and eliminating unwanted data. Can be done manually or automatically with an appropriate software package
Signal	The information required from the imaging system, e.g. the radiograph, the minimum size of the object that must be visible
Spatial Data	Gives the position of the varying intensities (brightness) across an image
Spatial Frequency	Object size, measured in line pairs per millimetre
Spatial Resolution	The smallest part of an image that can be seen
Window	The range of colour (or grey) scale values displayed on a digital image

Digitising an Analogue Image

An Analogue Image	• Two dimensional image • Different shades of grey • The shading is continuous throughout the image
A Digital Image	• Two dimensional image • Different shades of grey (or colours – red, green blue) • The grey is made up of discrete areas or pixels
Changing an Analogue Image to a Digital Image	• Take the analogue image • Imagine a grid superimposed over the image – usually 512 × 512 or 1024 × 1024 • The brightness (amount of grey) in each square (pixel) in the image is measured • Each pixel can then be given a number representing the brightness/shade of grey • Usually each pixel is given a value between 0 and 256 • This gives the image numerical values, i.e. digitises it
Nyquist Theorem	States that an analogue signal waveform may be reconstructed without error from a sample which is equal to, or greater than, twice the highest frequency in the analogue signal, e.g. • To digitally convert a 2 MHz signal, a sample must be taken at 4 MHz • To give a resolution of 5 line pairs per millimeter, each line pair equals 0.2 mm therefore a pixel sample must be measured at 0.1 mm intervals
Fourier Transform	• A method of mathematically changing data, e.g. changing spatial data to frequency data • Spatial data give the position of the varying intensities (brightness) across an image

Image Enhancement

	Methods of manipulating the pixel values to improve or enhance the area of interest in the image
Windowing	• Process of using the pixels to make an image • 256 shades of grey are usually assigned • But the human eye can only determine about 100 shades of grey • The shades of grey can be distributed over a wide or a narrow range of pixels
Narrow Window	• The grey is distributed over a narrow range of units • The central unit is the average pixel number for the structure of interest • If the average pixel was 50 and a narrow window of 170 was selected, then pixels of 85 (half 170) above and below 50 would be used • Therefore the grey scale would extend from – 35 to 135 • Any readings below – 35 would be pure black • Any readings above 135 would be pure white
Wide Window	• The grey is distributed over a wide range of pixels • The central unit is the average pixel number for the structure of interest • If the average pixel was 400 and a wide window of 2000 was selected, then pixels of 1000 (half 2000) above and below 400 would be used • Therefore the grey scale would extend from – 600 to 1400 • Any readings below – 600 would be pure black • Any readings above 1400 would be pure white

Adjusting Noise and Contrast

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Signal to Noise Ratio	Image quality may be defined as the signal to noise ratio: The signal is the information required from the imaging system • The signal can be defined as the minimum size of the object that must be visible The noise is anything that may detract from that signal • The noise, on the monitor, could be defined as the graininess of the image
Image Quality	• If the sharpness of the system is increased, the visually disturbing noise will also increase, as now the system is resolving the noise better as well as giving a sharper signal • If the contrast is increased the signal will appear to be clearer, but so will the noise • To compensate for all the factors is impossible, therefore the final result has to be a compromise (Fig. 7.1)
Contrast	A radiograph is the product of a transfer of information. During this transfer it is exposed to a number of different influences. Contrast helps to determine the quality of the radiograph There are three principal ‘types’ of contrast • Subject contrast • Image contrast • Radiographic contrast
Subject Contrast	Subject contrast (Fig. 7.2) can be defined as the ratio of the emergent intensities, i.e.: • This is caused by differential attenuation and absorption of the X-ray beam as it passes through the patient (i.e. the subject) • It is responsible for the differing intensities of the emergent X-ray beam, and therefore the exposures that eventually reach the film • Bone attenuates more of the beam than the fatty tissue and therefore the emergent intensity in the area below the bone is less than the surrounding fatty tissue • The film receives less exposure and produces a lower image density when compared to the fatty tissue areas
Factors Affecting Subject Contrast
Different Thicknesses of the Same Tissue Type	Subject contrast is the ratio of the intensity that has passed through the thin part, compared with the thicker part The thicker of the two will: • Attenuate more of the beam • Allow less exposure to reach the film
Different Densities of the Same Tissue with the Same Volume but at a Higher Density	Subject contrast is the ratio of the intensity that has passed through the less dense part, compared with the denser part The higher density will: • Attenuate more of the beam • Reduce the intensity of the emergent beam
Different Atomic Numbers of Different Tissues	The higher the atomic number: • The more the attenuation of the incident X-ray beam • The less the intensity of the emergent beam
Different Atomic Numbers of Different Tissues	Note At the energies used in diagnostic radiography, photoelectric absorption predominates and is the largest contributing factor to subject contrast
Radiation Quality – The kiloVoltage (kV) Set for the Exposure