Biomed Imaging Interv J 2006; 2(1):e6
doi: 10.2349/biij.2.1.e6
© 2006 Biomedical Imaging and
Intervention Journal
 TECHNOLOGY
IN IMAGING TUTORIAL
Image file formats LK
Tan, MBiomedEng
Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
INTRODUCTION This tutorial
is about image file formats: what are they, what are they used
for, what are their differences and how we choose between them.
The tutorial assumes a basic understanding of general digital
imaging; of which a quick summary of important features is provided
below.
Pixel: a digital image is represented as a rectangular
grid of dots, where each dot has a specific spatial position
and is colour defined. Each of these dots is known as pixels,
and represents the smallest unit in a digital image.
Matrix Size: the dimensions of a digital image; usually
specified in terms of its width and height in pixels. For an
image with square pixels, the matrix size also works to define
the image aspect ratio.
Spatial Resolution: a measure of the amount of spatial
detail in a digital image. Resolution is stated as a ratio of
physical to sampled dimensions, usually in the form of pixels
per cm or dots per inch. The stated physical dimensions depend
on whether the application is for acquisition or output.
Colour Space: colour space or colour specification
system is the method used to represent colour. Greyscale or
monochrome images usually use a single intensity gradient. Colour
images however, often encode colour in multiple channels, combining
them to represent an individual colour. Common examples include
Red-Green-Blue (RGB) and Cyan-Magenta-Yellow-Black (CMYK).
Bit Depth: bit or colour depth may be thought of
as colour resolution, i.e., a measure of the amount of colour
detail in a digital image. Bit depth states the number of computer
storage units (bits) used to represent the colour of each individual
pixel in an image. Common values are 24 bits per pixel (bpp)
for RGB colour images (8 bits per channel), and 8 bpp for greyscale
images. Medical greyscale images often exceed 8 bpp, with 10
bpp and 12 bpp being the most common.
WHAT IS AN IMAGE FILE FORMAT?
At its most basic level, computers store and work on digital
values of zeros and ones, known as bits. These bits of data
are then used to represent meaningful information, depending
on the context. For example, the bit sequence 01000001 might
represent the number 65 in a calculator program, while the same
sequence might represent the letter ‘A’ in a word
processor program, or the colour ‘dark grey’ in
a graphics program.
As eluded to above, without contextual information, data in
a computer becomes meaningless. Information about the data (known
as metadata) is just as important as the data itself.
An image file format is a standardised specification to encode
information about an image into bits of data for storage. In
a nutshell, an image saved and encoded to a known image format
identifies itself as an image, and provides useful information
such as its matrix size and bit depth, to ease interaction with
the file. Any program which adheres to the format standard may
then open the file and display the image.
BASIC TYPES OF IMAGE FORMATS
Most people categorise images by their visual content. For
example, x-ray films are medical images, photos of hills and
valleys are landscape images, paintings are artistic images
and so on. In contrast, computers are largely unaware of such
visual contextual information, instead categorizing images by
their method of representation. The two fundamental image format
types are raster images and vector images (some formats however,
allow a mix of the two).
Raster
A raster image, or bitmap, is the more commonly encountered
representation form. A bitmap represents an image via a rectangular
grid of pixels, where each individual pixel’s spatial
location and colour is defined.
Raster images may be thought of as analogous to the human
eye, which ‘sees’ via a large cluster of light sensitive
cells in the retina. Like a pixel, each cell has a specific
spatial location and measures the frequency (colour) and intensity
(brightness) of the light at that spot (Figure 1).
[View this figure] |
Figure 1 (a) A typical bitmap image.
When viewed from a distance, the image appears smooth
and continuous; (b) the eye region zoomed in by 10x.
Individual pixels – the atomic units that make
up the image – are revealed. |
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The vast majority of medical images are in raster format, which
is the focus of this tutorial. Examples of raster formats are
BMP, GIF, JPEG, and TIFF.
Vector
A vector image, or geometric image, represents an image mathematically
via the use of geometrical primitives such as points, lines,
curves and polygons (Figure 2). In a sense, one may consider
a vector image as storing information regarding the shapes in
an image rather than the raw image itself. Interestingly, the
brain appears to represent images in the same way: we regularly
recognize an image by identifying individual objects in the
image by pattern and shape.
[View this figure] |
Figure 2 (a) Clean lines and shapes
are characteristic of a typical vector image; (b)
the image broken down into its primitives, consisting
of geometric shapes and lines. Line thickness, fills
and colour are specified. |
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An example of a vector image we commonly use is a textual font
like Arial or Helvetica. Although most of us would not think
of a font in the sense of an ‘image’, a font fulfils
all the criteria of a vector image: each character is described
by a series of geometric curves and lines. Fonts also demonstrate
one of the biggest advantages of vector images: it is scale
independent i.e., a font can be scaled to any size without loss
in sharpness or detail (Figure 3).
[View this figure] |
Figure 3 A sentence of text rendered
as a bitmap (a) and vector (b) image. When zoomed
in by 5x, the bitmap image appears fuzzy and blurry
as the resolution has dropped. In contrast, the vector
text remains sharp throughout. |
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Vector images are not common in the medical field, and will
not be covered in this tutorial. An example of their usage is
the graphical treatment plan in radiotherapy. Examples of vector
formats are WMF, AI, EPS, and SVG.
IMAGE PARAMETERS
As mentioned previously, an image file stores the raw image
pixel data and the metadata, or information about the image.
Exactly what metadata to store, and how to do so, depend on
the format used. At a minimum, most formats store information
regarding the matrix size, colour space, and bit depth. Other
common metadata include:
Compression type: to save storage space, most formats
allow for data compression to be applied on the image data.
The two fundamental types of data compression are lossless compression
and lossy compression, which will be elaborated further in the
following section.
Dimensions: certain formats allow for multiple images to be
stored in the same file. The motive for doing so however, varies
from format to format. For example, formats which support animation
will display each image rapidly in sequence as individual animation
frames (Ultrasound clip). Other formats, which support 3D data,
may use each image as an individual section of the entire volumetric
set (CT) (Figure 4).
[View this figure] |
Figure 4 (a) Ultrasound clips use
the 3rd dimension to represent time; (b) CT volumetric
images use the 3rd dimension to represent depth. |
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Layers: layers are somewhat similar to dimensions
in which they allow for multiple images to be stored in the
same file. The difference is that layers are meant to be merged
and viewed as a single image, but with the ability to hide individual
layers at will. This is useful for applications such as overlays,
where the textual information is saved as a separate layer from
the actual image of interest (Figure 5).
[View this figure] |
Figure 5 (a, b, c) Separate image
layers; (d) a composite of the three layers on the
left, where individual layers can “switched”
on and off at will. |
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Others: other miscellaneous metadata include date
and time of creation, copyright information, comments and others.
The importance of metadata cannot be overstated, as it is vital
for proper reconstruction of the image. For example, given a
raw image file that is 200 bits in size, there is no way to
tell whether the stored image is a 1x2 portrait (10x20 pixels),
a 2x1 landscape (20x10 pixels) or indeed anything at all.
BITMAP IMAGE COMPRESSION
Consider a typical uncompressed CT image – 512 x 512
pixels in size at 12 bits per pixel. Ignoring any extra metadata,
the image would need a minimum of 3,145,728 bits or 393,216
bytes of storage space. Multiply the storage needed for a 100
slice study and 10 such studies a week, it is easy to see why
digital storage space is a real concern when working with digital
imaging. Rather than blindly purchasing hardware to increase
the available storage capacity, it makes sense to find ways
of utilizing the available storage space more efficiently.
The idea behind image compression is to take advantage of
redundancies in the image data, and re-encode the data in such
a way to make it more compact. The two fundamental types of
data compression are lossless and lossy compression.
Lossless
Lossless compression works to reduce mathematical redundancy.
This means the algorithm searches for repeating patterns or
sequences in the images, and reduces them to a compact formula.
For example, in a typical CT image, the surrounding edges are
usually totally black (air), which would be represented by a
long sequence of zeros (0 0 0 0 0 0 0 0 0 0 …). The lossless
algorithm would detect the sequence and replace it with an encoded
formula of the form ‘repeat zero ten times’ (Figure
6).
[View this figure] |
Figure 6 Lossless compression works
by taking advantage of patterns and repetitions in
data; The long stream of zeros (black) in line profile
A–A will easily yield huge compression savings,
but the seemingly random profile of B–B will
see little benefit. |
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When the compressed image is decompressed for viewing, the
resulting image is identical to the original source image, i.e.,
no information was lost in the compression process. Lossless
formats typically achieve 1:2 space savings or so, and work
best with relatively simple images such as diagrams and line
art; images with clean lines and flat colour.
Lossy
Lossy compression works to reduce perceptual redundancy. This
means the algorithm takes into account limitations of the human
eye, and discards data that is deemed nonessential to the perceptual
quality of the overall image. For example, the human eye is
less sensitive to colour details as well as very bright and
very dark tones. The lossy algorithm might then reduce the spatial
resolution of the colour channels, and smooth the parts of the
image that are very bright and very dark.
It should be emphasised that unlike lossless compression,
the final decoded lossy image is not identical to the original
source. The more aggressive the compression, the more information
is discarded and the more noticeable the difference between
the compressed image and the original. For this reason however,
lossy compression typically achieves better results, at around
1:10 space savings or so. Lossy compression works best with
photographic images, or images comprised of gradients and tones
with few sharp edges (Figure 7).
[View this figure] |
Figure 7 (a, b, c) Lossy compression
artifacts: the sample image is saved at progressively
higher compression levels, starting at 1:1, then 1:10,
then 1:30. Even at 1:30, the image remains very legible,
though obvious visual glitches are present; (d) the
1:30 compressed image when zoomed at 5x. There is
obvious smudging of detail, as well as a “blocking”
artifact; a key characteristic of the JPEG lossy compression
format.
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COMMON BITMAP IMAGE FILE FORMATS
BMP – Windows Bitmap Format
BMP was introduced by Microsoft® to be the native bitmap
image format for their Microsoft Windows® environment. Being
a relatively simple format, BMP lacks many features of other,
more robust formats, but is supported by most applications and
is often thought of as the lowest common denominator format
for interchanging images between programs.
JPEG – Joint Photographic Experts Group
Developed around 1986, JPEG is the de facto standard
for lossy image compression. In common usage however, JPEG is
often used to refer to the JFIF (JPEG File Interchange Format)
image format, which utilises JPEG compression. Thus, when someone
refers to a JPEG file, she actually means a JFIF file; correspondingly,
it is possible for other image formats to utilize JPEG compression
as well.
TIFF – Tagged Image File Format
TIFF was designed to be a highly extensible file format: capabilities
could be added to any file by the use of ‘tags’,
hence the name ‘tagged image file format’. This
resulted in TIFF becoming one of the most featured rich formats,
but conversely resulting in many incompatibility issues, where
only a subset of applications supported the more complex or
esoteric features such as multi-page encoding or lossy compression.
DICOM – Digital Imaging and Communications in
Medicine
In contrast to the aforementioned general purpose image formats,
DICOM was designed specifically for use in the medical industry,
defining a specific file format and a set of communication protocols.
DICOM shares similarities to TIFF in its ability for extension
via the use of custom tags. Unlike TIFF however, most extensions
revolved around additional information associated to the image
(e.g., modality name, patient birth date and physician in charge)
rather than additional features.
RECOMMENDATIONS
There are two main applications for saving images: storing
the original source images and making secondary copies.
All devices which generate digital images have an in-built
native image file format. For most medical modalities such as
CT and MRI, this would usually be DICOM, whereas digital cameras
and the like might use JPEG. For archiving, source images should
always be left in their original format.
Transcoding from one format to another always carries the
risk of information loss. It might be tempting to convert a
DICOM file to TIFF or PNG to take advantage of the better lossless
compression performance, but the conversion would strip out
unsupported metadata such as patient information. Transcoding
from a lossless format to a lossy format carries an even greater
danger, as the quantizing nature of the lossy algorithm can
be unpredictable. Small details in the original image could
easily be discarded as imperceptible noise, and the information
lost permanently.
For medical imaging, primary diagnosis should only be carried
out on the original source images, as they are guaranteed authentic.
For many other applications however, perfect reproducibility
is not a requirement, emphasis is placed on storage size and
convenience instead. Examples of such usage are images in presentations,
general purpose review and so on. We group these usages as secondary
copies.
When saving images for secondary copy use, the choice of format
is determined primarily on the content of the image. In general,
‘photographic’ images or images with smooth tones
and few sharp edges (this includes most medical images) are
best compressed with a lossy format such as JPEG. In contrast,
images that consist mostly of clean lines and solid colour such
as diagrams and text will be best compressed by a lossless format
such as TIFF (Figure 8).
[View this figure] |
Figure 8 (a) A photo originally
in a lossless format (b) when compressed to JPEG,
the photo is 10% of its original file size, with little
perceptible difference. (c) Simple images compress
well in GIF (d) JPEG manages to attain a similar file
size, but at the cost of severe image artefacts.
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REFERENCES
- Murray JD, van Ryper W. Encyclopedia of Graphics File Formats. 2nd ed. O'Reilly & Associates Inc. 1996.
- Brown CW, Shepherd BJ. Graphics File Formats: Reference and Guide. Manning Publications. 1995.
- Vector Graphics. Wikipedia. 2005-12-17; http://en.wikipedia.org/ w/index.php?title=Vector_graphics&oldid=31680738
[ FREE Full text ]
- JPEG. Wikipedia. 2005-12-18; http://en.wikipedia.org/ w/index.php?title=JPEG&oldid=31811842
[ FREE Full text ]
- Graphics File Formats FAQ. 1997-1-20; http://www.faqs.org/ faqs/graphics/
[ FREE Full text ]
- Common Image File Formats. Cornell University Library / Research Department. 2002; http://www.library.cornell.edu/ preservation/tutorial/presentation/table7-1.html
[ FREE Full text ]
- Gormish M. Gormish Notes of JPEG2000. 2004-9-19; http://www.crc.ricoh.com/~gormish/jpeg2000.html
[ FREE Full text ]
Received 26 October 2005; received
in revised form 6 January 2006; accepted 20 January 2006
Correspondence: Department of Biomedical
Imaging, Faculty of Medicine, University of Malaya, 50603
Kuala Lumpur, Malaysia. Tel.: +603-79502091; Fax.: +603-79581973;
E-mail: lktan@um.edu.my
(Li-Kuo Tan).
Please cite as: LK Tan, Image file formats,
Biomed Imaging Interv J 2006;2(1):e6
<URL: http://www.biij.org/2006/1/e6/>
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