Our Feature Extraction Skills
Performing accurate measurements on objects in images is
fundamental to solving many image analysis problems. Our group has
considerable experience in finding novel measures which extract the
maximum information from the available data. These measurements may be
simple, such as the number, size, or colour of objects, or more complicated,
such as the shape, connectivity, or appearance (texture) of objects (there
are over 100 different measurements describing image texture alone).
Additional measurements might describe the spatial arrangement or
distribution of objects in a scene, or the statistical distribution of
properties across many objects. Sometimes it is well established
beforehand which features of the image need to be measured, at other times
suitable measurements are "discovered" from a large number of pre-computed
possibilities.
For example, in the
melanoma diagnosis project which
sought to classify skin lesions, over 600 features were considered for
inclusion in the final algorithm. These were later culled to the 80 best
based on performance, and later to less than 12 for the final model.
In other cases, a specific operator can be constructed to
measure some particular property of the data - for example the dark
spots (melanocytes) on the boundary of a melanoma can be detected and
measured by a novel boundary-restricted top-hat operator.
Techniques
There are many classes of features and each has various
techniques for measurement. In addition higher order features are formed
by combinations or distributions of the simpler measurements. For example:
-
object size (area, volume, perimeter, surface) - obtained
by counting pixels
-
object shape - obtained by characterising the border -
Fourier descriptors, invariant moments, shape measures, skeletons, edge
abruptness
-
object colour - description in colour-space, integrated optical density,
absolute and relative colours
-
object appearance/texture - colour variation in pixel neighbourhoods -
co-occurrence matrices, run lengths, fractal measures, statistical geometric features
-
Parameters from fitted statistical models - used within objects for
texture, eg. Markov Random fields - or to describe placement of objects
within a scene (eg Poisson models)
-
Distributional parameters - moments: mean, variance, skewness, kurtosis,
median, inter-quartile range - used to describe statistical distributions
of the more fundamental features, for example within a scene.
We have libraries of software available to implement all
these measurements and many more on image data. Examples
Feature extraction was an important part of the following
projects:
For further information, please contact:
Pascal Vallotton
Leader, Biotech Imaging
CSIRO Mathematics, Informatics and Statistics
Locked Bag 17, North Ryde NSW 1670 AUSTRALIA
Phone: +61 (0)2 9325 3208
Fax: +61 (0)2 9325 3200
Email: pascal.vallotton@csiro.au
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