Digital Imaging Systems

Medical Imaging Systems Projection Radiography Computed Tomography Nuclear Medicine Ultrasound Imaging Magnetic Resonance Imaging

Projection Radiography

Computed Tomography

Emission Tomography

Ultrasound Imaging

Magnetic Resonance Imaging

Medical Imaging Signals X-ray transmission through the body Gamma ray emission from within the body Ultrasound echoes Nuclear magnetic resonance induction

Computed Radiography

Digital Radiography

Analogue v Digital Signals The “real world” information (signal) is often in analogue form. Computer deals with digital numbers. In order to transfer, manipulation, display, storage of the real world information in computers, the analogue signals need to be converted into a form that is used by computer, that is in digital form.

Analogue Analogue information come from “real world” objects light reflected from object x and  radiation passing through the body ultrasound / radio waves Electrical signals formed in recording the above radiations

Analogue (cont.) Analogue is a “continuous” signal in that if you were to measure it between 2 points, you would have an infinite number of values

Analogue (cont.) We, as humans, can only perceive analogue information We convert analogue data to digital for use in computers but we also need to reconvert it back to analogue for humans to perceive, eg light sound

Digital Digital data is discrete, compared to continuous Typified by steps – finite number of values between 2 points

Digital (cont.) More easily manipulated and stored – hence well suited for use in computers Can be copied exactly (with error checks) where as analogue information looses quality every time it is copied eg photocopies, film copies use analogue techniques Generally can not be viewed by humans

Forms of Digital Data Bistable (Bit) Consists of 2 values 0 or 1 off or on magnetised or not magnetised laser hole or no laser hole In terms of images, black or white – no shades of grey

Forms of Digital Data One bistable value is called a “bit” – a binary digit A bit is of little value by itself, but can be one of several bit to form a “byte”. A byte is generally referred to 8 bits. Byte – with 4 bits example: 1234 values

Forms of Digital Data (cont.) Number of values in a byte depends on its “bit depth” 1 value – 2 1 = 2 2 values – 2 2 = 4 8 values – 2 8 = values – 2 10 = 1024 Commonly, especially in imaging, values will range from 0 to 2 n – 1, where n is the bit depth eg. 8 bit depth – values range from 0 to 255

Forms of Digital Data (cont.) In terms of computing, common values of the bit depth are 8 – 256 values 16 – 65,536 values 32 – 4,294,967,296 values In digital images 8 – 256 values (simple grey scale images) 10 – 1,024 values (medical images) 12 – 4,096 values (medical images) 24 – 16,777,216 values (colour images)

Digital Computer Hardware ●Input devices ●ADC (analogue to digital converters) – from CR, MRI, CT, SPECT, PET, U/S, film scanners ● Keyboards ● Storage Volatile – RAM Non-volatile – ROM, hard drives, CD, MOD, tape Stored as bits Measurement – Kbytes, MB, GB

Computer Hardware Structure

Digital Computer Hardware (cont.) CPU Calculations Control of data flow Measurement – speed in calculations / second Hertz – MHz, GHz Output devices Must pass through DAC – digital to analogue conversion monitors, printers, sound speakers

Central Processing Unit

Computer Softwares System Control Software – Operating Systems Programming Software – Programming Languages Application Software – Digital Imaging Applications Graphical User Interface – IDL, Matlab

Basics of Images Images can be analogue or digital Analogue Photographs X-ray / nuclear medicine films Can not be manipulated Digital Stored in memory (can be displayed on a monitor) Can be manipulated, copied exactly Can be grey scale (of any bit depth) or colour (24 bit depth)

Medical Image Conversion Process Patient Image Acquisition Analogue Image ADC Digital Image Processing Digital Image DAC Digital to Analogue Conv. Diagnostic Image Viewing Analogue Digital Analogue

ADC Process ADC Process consist of 4 stages: Sampling Sensing Quantising Coding This process converts analogue information to digital data, i.e. discrete values / integers.

ADC Process

ADC Process - Sampling In the sampling section of the ADC process, a sampling rate needs to be established. This is the rate at which the analogue information is “read” or sampled The higher the sampling rate, the more accurately the digital data will represent the analogue information. In a digital image, the rate determines the no. of pixels in a row, i.e. the spatial resolution of the image.

ADC Process - Sampling Sampling rate and aperture size are similar Aperture size is the time interval between sampling points and given by: sampling rate = 1 aperture Increase the sampling rate, aperture size decreases.

ADC Process - Sensing Sensing is the act of reading the analogue information, at the preset sampling rate. As an example, the analogue information could be a voltage between 0 and +5 volts. At that particular sampling point, the voltage will be “sensed”.

ADC Process - Quantising Quantising is setting the number of digital values that are available, i.e. the bit depth. As an example, if the bit depth is 8, there are 256 possible values that voltage (from previously) of between 0 and +5 V can be converted to. Quantising part of the ADC is responsible for the contrast resolution of the image.

ADC Process - Coding Coding converts the analogue value to the equivalent digital value. From the example previously, 0 to +5V at 8 bit depth Each digital step = 5V  256 = V eg. voltage sampled = V is coded at Values must be discrete so the value is rounded down to 76. The pixel value at that point is 76.

Errors in the ADC Process Sampling rate (and hence aperture size) Higher the rate, the smaller the pixel size truer representation of the object larger file size Low sampling rate leads to errors resulting from under-sampling. Nyquist's theorem sets minimum sampling rate.

Nyquist's Theorem Nyquist's theorem is quite simple: it says that we must sample at least twice as as fast as the highest frequency in the signal. In imaging, the sampling points must be at half of the distance of the size of the smallest object Under-sampling

Sampling in Images ObjectSample Sample size:- - & object similar in size - smaller than object Sampling Process Image Representation edge representation of the edge

Errors – Sampling Rate Under-sampling errors result in aliasing Aliasing, in a static digital image, appears as a “blocky” image or “steps” along edges within the image.

Aliasing resulting from under-sampling

Errors in the ADC Process Quantisation Error These result from not having set an adequate bit depth to the ADC process The greater the number of quantisation values (bit depth) the greater is the accuracy of representation of the analogue information

Quantisation Error Closely related is the quantisation rounding error. eg. 8 bit depth vs 4 bit depth previously – 8 bit depth V is coded at ie. pixel value of 76 4 bit depth - digital step = 5V  16 = V voltage sampled = V  V = which is rounded up ie. pixel value of 5 There are error in this rounding process. These are greater the smaller the number of quantisation values.

Quantisation Error The rounding is greater with a smaller bit depth Max quantisation errors = rounding value x 100 no. of quantisation values Bit DepthNo of Values Max Quantisation Error %

4 Bit Depth 2 Bit Depth 8 Bit Depth

Quantisation Error If the image display contrast has been optimised for the viewing condition, quantisation errors will not appear to the view until the bit depth is below 5 (32 values). A typical human observer can only perceive approx 30 shades of grey, hence an optimised image at 6 bit depth will appear the same as 10 bit depth image.

Quantisation Error Given the above, why in medical images do we use bit depths of 10 or 12? A 10 or 12 bit depth, eg. in CT, will give a more accurate representation of the intensity of the anatomy’s ability to attenuate the beam Also, how do we know what anatomy we need to have the displayed contrast optimised for view. Do we optimise viewing contrast for, eg. in a CT, the entire slice or for the liver

Basics of Images Images are representations of “real world” objects Photo of a friend is a representation of them Radiograph / nuclear medicine scan is a representation of the anatomy and / or physiology of that patient Must be able to be perceived as that object Can be analogue or digital

Basics of Images All images are 2 dimensional – with the possible exception of holograms. Can use “tricks” to be perceived as 3D analogue – cross – eyed until perceive depth or hidden objects coloured lens digital – depth perception – shading, perspective colour lens

Basics of Digital Images Digital images are a 2D array of values – often thought of having X and Y axes or row and columns

Basics of Digital Images (cont.) X and Y axes (row & columns) do not have to be of the same length Each Cartesian point or pixel (picture element) in an image has a value that is an integer and can be described as: I (x, y) eg I (3,6) = 149 from previous array In the previous array, X & Y axes started at 0, but in some image formats, start at 1.

Basics of Digital Images (cont.) Maximum value of I (x, y) will depend upon the bit depth and equal 2 n – 1, where n is the bit depth eg. in 8 bit depth image, integer values range from 0 to 255 This is often referred to as the depth of the image.

Intensity map of pixel values. Note: max value <= 255 can use this mapping to visualise contour boundaries Note: the flat area of zeros represent black in the image

Basics of Digital Images (cont.) The previous array or image was a grey scale image. It had intensities ranging from 0 to 255, which when converted to analogue for humans to perceive, will give a variation of intensities, normally viewed from white (255) to black (0). Could be from a colour to no colour (black)

Basics of Digital Images (cont.) Concept of digital images to display digital image inside monitor (not visible) 3 x colour guns (R,G, B) R – intensity of 128 G – intensity of 128 B – intensity of 128 display on monitor R OUTPUT Gshade of grey B (value of 128) 128 Pixel Value I (x,y) O (x,y)

Basics of Colour Digital Images Colour images are the equivalent of 3 grey scale images Each array represents the values for red, green and blue Red Green Blue

Basics of Colour Digital Images The notation is Ic (x, y) where Ic is the colour Each colour array is often referred to as a band The visible displayed colour is a mix (additive) of the 3 colour values eg blue (0, 0, 255) Possible no. of colours – 16,777,216

Basics of Colour Digital Images (cont.) Concept of colour digital images to display digital image inside monitor 3 x colour arrays (not visible) 3 x colour guns (R,G, B) R – intensity = 109 G – intensity = 249 B – intensity = 65 display on monitor R OUTPUT Ghue of additive B colour R = 109 G = 249 B = 65 Pixel Value I (x,y)

red, green & blue colour bands image – mix of the 3 bands

Digital Image Files Image are stored as a specific file format, eg as jpeg (jpg), gif, tiff, targa (tga - which is used in Imaging Concepts), etc. Medical images are now using a format called DICOM The image file itself contains 2 separate areas image data header

Digital Image Files (cont.) Header stores information about the type of format (see later) the number of rows and columns colour or grey scale the location in the first pixel value in the DICOM format for medical imaging – includes:- imaging modality, patient details inc. space for report and reasons for the test.

Digital Image Files (cont.) Image file size is determined by both the image data and the header size. Image data size is determined by the number of rows x columns x bit depth (in bytes) eg rows and columns 1000 x 1000 x 8 bit depth (1 byte) = 1 MB 1000 x 1000 x 10 (or 12) bit (2 bytes) = 2 MB Colour image (3 bands) 1000 x 1000 x 8 bit (1 byte) = 3 MB

The file size must then add in the size of the header (in bytes) Image files can become very large so means of making them smaller in size is commonly used. This is called compression. (This will discussed in detail later)

Other colour image formats To save storage space, some other colour file formats have been developed. Colour palettes are used to replace the 3 separate bands in a “normal” colour image The palette is a separate list of colour values, RGB values (intensities), that are used in that image. The I (x,y) value “looks up” the value of a colour in the palette.

Only the colours used in the image have values in the palette eg. if the image has 3 colours, the palette will only have 3 rows. Even if a large number of colours are used, storage space is less than the format that uses 3 band of colours Image Palette

Grey / Colour Manipulation The pixel values, I (x,y), in the image are stored on the hard drive of the computer and do not change. This is the store image, I s (x,y) The viewed grey scale and colour can be changed – as seen on the monitor This is achieved through the use of “look-up tables” (LUT) I s (x,y) is compared to a displayed value, I d (x,y), which is used to the display intensity.

Look-Up Tables A means of altering the value of the stored pixel, so it can be displayed as a different value ie. a different displayed intensity. All the potential pixel values, in an 8 bit depth image – 256 values, are put on one side of the table Any mathematical calculation to alter the output values is then applied to give output values These values are put on the other side of the table The display “looks up” the pixel value and then finds the corresponding output value

Look-Up Table Operations:- x 1.5 calculation x 1 calculation Graphical display

Grey scale display Grey scale images have one band (channel) of pixel values – uses 1 LUT The output value from the LUT goes to the 3 colour (RGB) guns in the monitor As the intensities of the RGB colour guns are equal, a grey (white  black) colour will be perceived on the monitor Displayed greys can be manipulated by altering the operation of the LUT

Grey scale display 4 Pixel Value Stored Image I s (x, y) colour guns - monitor RGBRGB 14 displayed value Displayed grey intensity 14 Displayed Image I d (x, y)

Colour display Colour images have 3 bands (channels) of pixel values and uses 3 LUT’s – 1 for each band 3 pixel values form each band, same x,y coordinates, are the input values for each RGB LUT. The output value from the LUT’s goes to the corresponding colour (RGB) guns in the monitor The values of the intensities of the RGB colour guns will often not be equal, hence a colour (rather than grey) will be perceived on the monitor Displayed colours can be manipulated by altering the LUT’s of each band.

Colour display Stored Image I s (x, y) colour guns - monitor RGBRGB displayed colour R = 109 G = 249 B = 65 Red Green Blue to display R = 119 G = 255 B = 75 displayed RGB values Displayed Image I d (x, y)

Red Green Blue Red Green Blue Actual Look-Up Table Graphical display of the Look-Up Table

Psuedo-colour display Pseudo-colour images have one band (channel) of pixel values but use 3 LUT’s The output value from the LUT’s goes to the corresponding colour (RGB) guns in the monitor The values of the intensities of the RGB colour guns will often not be equal, hence a colour will be perceived on the monitor Displayed colours can be manipulated by altering the LUT’s of each band.

Red Green Blue 4 Stored Image I s (x, y) Psuedo-colour display colour guns - monitor RGBRGB displayed colour R = 4 G = 8 B = 251 displayed RGB values Displayed Image I d (x, y)

Red Green Blue Actual Look-Up Tables Graphical display of the 3 Look-Up Tables Plot of LUT - Red Plot of LUT - Green Plot of LUT - Blue