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include:
Patient (code) correspondence with the initial
data of his/her examination.
Multiple examinations per patient (through
appropriate code).
Data display with multiple horizontal sections
in icon size.
Data storage after their process (in DICOM or
other format) in the hard disk of the computer
system.
Determination of reference data from the user.
Ability to support multiple data to align
common reference data.
2.2 Medical Data Preprocessing
Subsystem
The data preprocessing is an optional step. It applies
to data which are characterized by high levels of
noise and the containment is achieved by using the
appropriate filters. So, it is usual that before the
registration a re-sampling of one or both data sets
that have the same discretionary analysis is needed.
Thus in the subsystem an appropriate technique for
re-sampling is incorporated (
Unser, 1993). The data
pre-processing subsystem includes the segmentation
technique as developed. In this case, anatomical
information is extracted from the two data sets (for
example the external surfaces of the skull from both
CT and MR scanners), which is then used to perform
the registration.
2.2.1 Pre-processing Techniques
The acquired 3D data may include noise and/or
characterized by heterogeneous background. This
noise is undesirable and should be removed, without
the loss of significant anatomical information
contained in images. For noise reduction, suitable
filters are implemented to improve the quality of
images, which are applied on section based (two-
dimensional problem - 2D) (Gonzalez, 1993).
Specifically, within the subsystem the following
filters have been implemented to improve image
quality:
Mean filter: It is a low-pass filter which reduces
high-frequency noise in an image.
Median filter: it is another filter for noise
containment.
Gamma correction: The factor γ determines the
function which distributes the values of pixels,
according to the intensity of brightness of the
screen. The factor γ is equal to one when there
is a linear relationship between pixel values
and intensity of brightness. Images that appear
darker usually require the factor γ have values
larger than one, while those which appear
bright usually require the factor γ have values
smaller than one.
Histogram Equalization filter: it is a commonly
used technique for better visualization of the
diagnostic information of an image. In cases
where the biological tissue of interest shows
rates (different levels of gray), which vary
between certain limits in the digital image, the
visualization of the tissue is significantly
enhanced if the function which corresponds
the values of pixels in the image with
brightness in screen changes.
Adjust brightness and contrast: It is one of the
most basic functions for image editing. The
implementation of this subsystem provides the
opportunity to change the brightness and
contrast of images by the simple linear
transformation:
)
)
byxaIyxI +
,,'
(1)
Where I(x,y) is the pixel of the initial image with
coordinates (x,y) and I’(x,y) is the pixel of the
adjusted image.
2.3 Medical Data Alignment Subsystem
In many cases in the current clinical practice it is
desirable to combine information provided by two or
more imaging modalities or to monitor the
development of a treatment based on data collected
at different times by the same modality. In
particular, when monitoring the development of a
treatment, it is very often the imaging anatomical
structures displayed in two sets of data that have
been collected at different times to be characterized
by geometrical movements, revolutions, etc. It is
necessary to find an appropriate geometric
transformation, which achieves the spatial
coincidence of anatomical structures of the two
images. This process of finding the transformation is
called registration.
The medical data alignment subsystem consists
of a set of techniques for 3D registration of brain
data on surface based or using the levels of gray
(gray-based). Particular attention has been given to
the design of the automatic registration techniques.
Alternatively, there is the option of manual
registration using appropriate surface driving points
as selected by the expert.
Within the design of this subsystem three
registration techniques were implemented:
Automatic registration based on surfaces,
Automatic registration based on gray levels and
DIAGNOSIS - A Global Alignment and Fusion Medical System
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