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A Method for Improving Renogram Production and
Detection of Renal Pelvis using Mathematical
Morphology on Scintigraphic Images
Stefanos Xefteris
Electrical and Computer Engineering
Department
National Technical University of
Athens
Athens, Greece
xefteris@mail.ntua.gr
Konstantinos Tserpes
Electrical and Computer Engineering
Department
National Technical University of
Athens
Athens, Greece
tserpes@mail.ntua.gr
Theodora Varvarigou
Electrical and Computer Engineering
Department
National Technical University of
Athens
Athens,Greece
dora@telecom.ntua.gr
Abstract— Dynamic renal scintigraphy is a well-established
imaging technique in nuclear medicine, used to detail both the
organ's anatomy and function. However, the quality of the
produced scintigrams provides an often unreliable diagnostic tool
because of a rather bad signal-to-noise ratio and the fact that in
certain occasions the regions of interest are too concentrated
making it difficult for physician evaluation. The goal of this
paper is to achieve a more accurate production of the renal
activity graph, by avoiding the inclusion of image artifacts in the
detection process. This is achieved by treating pixels as points in
a two-dimensional Euclidean space, and exploiting set-theoretic
properties and morphological operators. The evaluation of the
method in a number of real patient’s scintigrams obtained in a
depth of 5 years, showed that, in the majority of the cases, it is
feasible to produce a more accurate renogram, for both kidneys
and the renal pelvis region, that was helpful for the interpretation
of the findings
Keywords: renal scintigraphy; mathematical morphology;
imaging; pattern analysis; detection
I. INTRODUCTION
Renal scintigraphy is a sensitive mean for detection,
evaluation and quantification of numerous renal conditions.
Scintigraphy is a diagnostic test in which a two-dimensional
picture of a body radiation source is obtained through the use
of radioisotopes. Radioisotopes are taken internally, and the
emitted radiation is captured by gamma cameras equipped with
a parallel-hole collimator to form two-dimensional images,
called scintigrams, that detail both the organ's anatomy and
function. Periodic capturing and post-processing of these
images produces a sequence of kidney instances throughout
time, along with a renogram - a graphic record that depicts the
brightness variation throughout time in the image. These may
help in the quantification of certain parameters of the renal
function, such as the effective renal plasma flow (ERPF), the
excretory index, the glomerular filtration rate (GFR), and the
differential renal function.
Through the assessment of the abovementioned metrics, the
physician can detect anatomic or functional abnormalities of
the kidneys or the urinary tract by interpreting images and/or
digital data of diagnostic quality. Such abnormalities include,
but are not limited to, the detection, evaluation and
quantification of possible urinary tract obstruction, the
detection and evaluation of renovascular disease, the detection
of pyelonephritis and parenchymal scarring, the detection and
evaluation of functional and anatomic abnormalities of
transplanted kidneys, the qualitative measurement of renal
function and the detection of congenital and acquired anatomic
renal abnormalities [1]
There are various quantitative renal function protocols
followed by physicians in order to detect different renal
abnormalities such as those mentioned above. A protocol
suitable for routine clinical use includes the injection to the
patient of some kind of renal tracer such as 99mTc-MAG3 and
the monitoring of its flow in the kidneys using a gamma
camera in order to evaluate three distinct phases: perfusion,
concentration and excretion [2]. These are depicted in the
resulted images and in the renogram which help the physician
to conduct visually two types of diagnoses:
• Quantitative analysis, in which the physician identifies
the existence of one or both kidneys, kidney deformities,
and abnormalities in the renal parenchyma and finally
• Renogram analysis, in which the physician examines the
renal behavior throughout time, i.e. if the absorption and
excretion of the radiopharmaceutical is normal.
The accurate diagnosis from the physician is subject to the
physician’s experience and ability to identify patterns. The
physician interprets the potential findings visually, examining
dynamic images, and through the washout curve shape
(concave vs. convex) of the renogram. Further, the diagnosis is
usually obscured by low image quality due to naturally inserted
noise [3]. It should be noted that a Crucial Region of Interest
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(ROI) in the scintigraphic images (scintigram) is restricted to a
small area of the kidney, called renal pelvis, which acts as the
main output funnel of the kidney. By examining the renal
pelvis area, the physician can evaluate the flow of the
radioisotope of the kidney.
In this paper we advocate that the above mentioned practice
can become more accurate if we deploy an image processing
technique that will process the renal images and isolate the ROI
(renal pelvis), filter the picture from unwanted artifacts inserted
by noise and finally estimate the flow of the renal tracer
through the production of the renogram (renal activity graph)
and of the activity graph of the renal pelvis. This is achieved by
a series of algorithms applied on the scintigram with the
purpose to measure the absorption and excretion of the
radioisotope. We argue that the application of the overall
algorithm is efficient in terms of performance and quality and
we compare our results with those produced by applying a
regular scintigraphy protocol without post-processing.
The paper is structured as such: Section 2 presents the
related work. Section 3 analyzes the proposed algorithm.
Section 4 presents details about the system implementation and
the evaluation dataset. Finally, Section 5 details the conclusions
reached through this endeavor.
II. RELATED WORK
Dynamic renal scintigraphy is a well-established imaging
technique in nuclear medicine [4]. The intervention of the
computer in the identification of important artifacts is usually
reaching to the point of the depiction of the brightness of the
images throughout time in the production of the renogram. The
resulting graph can provide information to the physician about
the flow of the radioisotope in the kidneys. However, several
approaches have been proposed in the literature for post-
processing of the results in order to assist the physician to
better interpret the results. Specifically, several studies ([4] [8]
have shown that the automatic definition of renal ROIs that
helps in minimizing user interaction in the evaluation of
dynamic renal scintigraphy is generally required in the practice
of nuclear medicine.
Authors in [9] state that it is a common practice to apply
post-processing tools to improve the quality of the images for
viewing, such as altering the display window levels and the use
of filters such as smoothing or Metz. ROIs can be used to mask
out areas of high uptake not related to the back such as the
kidneys and bladder as well as areas of physiological high
uptake within the back and pelvis such as tuber coxae, to allow
better visualization of other areas within the image. ROIs can
also be drawn around anatomical structures to give uptake
ratios.
Moreover, authors in [10] applied fuzzy methods in order to
extract time–activity curves corresponding to renal
parenchyma, renal pelvis, vascular and spatially homogeneous
background. Their method is applied to factor images of the
renal parenchyma and identifies fuzzy regions of interest in
contrast to those obtained manually in order to produce a more
accurate graph for physician evaluation. Similarly to what the
current paper proposes, the authors of [10] achieve the
exclusion of artifacts that do not add value to the creation of the
time-activity curve. However, their method is applied on a
different examination protocol and they aim into isolating the
activity ROIs rather than the ROIs that present the clinical
interest as the current paper suggests, based on well-established
practices.
In [11], the singular value decomposition method is
presented as a potential tool for analysis and semi-
quantification of scintigrams. The results conclude that it is
possible to “objectify” the interpretation of clinically relevant
information contained in the images. They reduce the signal-to-
noise ratio by forming the image with those singular vectors
that have the greatest impact in the decomposition they use.
Other approaches are focusing on filtering out noise from
the nuclear images. The most representative is [12] where the
authors used signal processing methods to obtain a
representation of the image in a domain where salient
information could be separated from noise. The information
that is contained in the noisy scintigrams is then transformed
into a small number of coefficients. In turn, the coefficients are
analyzed so as to derive their statistical significance. This way,
the authors managed to decompose the image to its signal and
noise components and to restore it once the noise components
were eliminated.
III. PRODUCTION OF ACTIVITY GRAPHS FOR COMPLETE
RENAL ACTIVITY AND RENAL PELVIS ACTIVITY
The isolation of the kidney ROI is the result of a workflow
that includes a sequence of small procedures and the execution
of a number of algorithms. This process is depicted in Figure 1.
Fig. 1. Sequence of Applied Algorithms
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In the following subsections we analyze the steps of the
algorithm in detail.
A. Detection of Kidney and Renal Pelvis
In this Section the main algorithm for the isolation of the
area in the image that depicts the kidney and the renal pelvis is
described. In brief, the developed algorithm analyzes the
scintigraphic image in order to identify the ROI and produces
the renogram and radiograph of the isolated renal pelvis. These
radiographs are then used for the measurements.
1) Isolating the Kidney Region
The boundary tracing algorithm is taking advantage of the
brighter regions of the original radiograph in order to detect the
contours that delineate the boundaries of the kidney. The
algorithm is performed on a binary image where nonzero
valued pixels belong to an object and zero value pixels
constitute the background.
Regions with high brightness that are out of the ROI, and
appearing in scintigraphic images in the extreme upper and
lower parts of the image (namely the heart and the bladder)
were isolated by cropping the extremities of the image. After
cropping the out-of-interest regions, a stretching function is
applied on the image, in order to enhance the overall brightness
of the image. Stretching is a procedure where we multiply the
brightness of all the pixels in the image by a factor α, where
1,
with Max_b=The maximum brightness value in the image.
So, after stretching, for every pixel in the image we have:
#ew Brightness Value= ⋅a Old Brightness Value
This resulted in a first approach of the isolating the ROI as
seen in Figure 2.
Fig. 2. Results after cropping extremes and stretching image brightness
The next step in the preparation of the image for the
application of the boundary tracing algorithm is the adaptive
equalization of the image, in order to enhance contrast. With
this procedure, we aim in further improvement of the clarity in
the image, and better brightness separation between the ROIs
and the rest of the image.
The result of adaptive equalization improves the contrast in
regions of the image, so that the resulting histogram follows as
much as possible, the normal distribution. Neighboring regions
are combined with bilinear interpolation to remove artificial
boundaries created by the equalization. We can see the results
of adaptive equalization in Figure 3.
Fig. 3. (left) Image before adaptive equalization, (right) image after
adaptive equalization
In order to better facilitate the application of the boundary
tracing algorithm, we have to split the image in two halves,
each containing one kidney. In a sample of more than eight
hundred (800) renal scintigrams taken in a clinic, the result was
that the image can be split in the exact center without affecting
the ROIs. So, from the initial matrix of 128x128 pixels, we
produce two 128x64 pixels matrices, each containing one
kidney.
The next step in the workflow is to estimate the image-
specific statistical properties, in order to further adjust the
image contrast, and then re-calculate them after the image
adjustment. The statistical measures used are: the mean value,
the median and the maximum of brightness for each of the two
matrices containing the kidneys. By applying image adjustment
we further improve the image contrast by transforming the
brightness values so that 1% of the image data is saturated in
high and low brightness.
After these preparatory procedures our image is now ready
for the detection of the ROIs. Our purpose here is to make the
kidney contours in the two matrices as “clear” as possible, and
remove isolated bright pixels in the images, in order to feed the
data to the Boundary Tracing Algorithm. By exploiting the
statistical properties of the images, i.e. the mean, the median
and the maximum brightness values, we compute a threshold
(specific and different for different scintigraphic images), under
which a pixel in the image is considered to be outside the ROI.
This method was applied to a database of a private doctor’s
clinic, on about 850 different scintigrams and procured
expected results in more than 90% of the sample. Another
method that was tried on these samples was the a priori
definition of a brightness threshold, only depending on
maximum brightness value and ranging from 120 to 130, was
found to be inadequate, producing good results in less than
60% of the sample.
The issue that rises, after the aforementioned procedures, is
that of –not only possible, but almost always present- isolated
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pixels that were bright enough to be included in the image.
Another issue that has to be tackled is also that of pixels inside
the ROIs that were not included because of their low brightness
(which is crucial for the final diagnosis, since low brightness
pixels inside the ROIs are indicative of a pathology).Thus, in
order to erase small or larger brightness regions, outside our
main ROIs and re-include regions that were removed but are
inside the ROIs, we apply binary mathematical morphology
operators on the images. In binary morphology the image is
considered to be a subset of an Euclidean space
d
R or the
integer grid
d
ℤ for a dimension d.
The morphology operators used are the clearing operator,
the majority detection operator and the erosion operator. The
clearing operator [13] removes isolated pixels that may still
exist in the image, as shown in Figure 4.
Fig. 4. Example of an Isolated Pixel
The “Majority detection” [13] operator is responsible for re-
filling “holes” inside the kidney regions, which were created
by false-positive extractions due to low brightness. The
Majority detection operator checks if a pixel has value 1 and at
least 5 neighbouring pixels have non-zero values. If not, the
pixel’s value is then set to zero, as shown in Figure 5.
Fig. 5. Pixels before and after mazority detection
Finally, we perform the Erosion morphological operator
[13], with the unitary structural element, in order to cover
regions at the edges of the kidneys, that may have been omitted
from the ROI. Erosion of a binary image A in the Euclidean
space E with a structural element B is defined by:
where Bz is the translation of B by the vector z, i.e
EzBbzbBz ∈∀∈+= }|{
With the erosion procedure we conclude all preparatory
steps concerning our ROIs and we are now ready to isolate
them from the background and apply the boundary tracing
algorithm. Thus, we set the brightness value of all pixels
outside the kidneys to zero. The result can be seen in Figure 6.
The next step in our process is the application of the
boundary tracing algorithm. Since we have “filled” all the
probable gaps inside the ROI’s, there is no chance that “holes”
will appear in the kidney regions and thus we will later take
into account every pixel belonging to them, using its brightness
value to produce the Renal Activity Graph. The boundary
tracing algorithm [14] returns the coordinates of the edges of
and area, beginning from a pixel on its boundary, as shown in
Figure 7.
Fig. 6. Kidney ROI’s isolated and zeroing of other regions
Fig. 7. Boundary tracing. (a) Connectivity 4, (b) Connectivity 8, (c)
tracing sequence in an area with connectivity 4, (d),(e) tracing sequence in an
area with connectivity 8 (f) boundary tracing in area with connectivity 8
(dotted lines indicate pixels tested during the application of the algorithm).
Now that the boundaries of the ROI’s have been traced, and
we have their coordinates, we proceed with the creation of two
masks, one for each kidney. The masks will be used as a
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blueprint for the kidneys in all 26 images of the scitintigraphy
test, applied to each one of them, in order to calculate the sum
of the brightness value of all pixels in them, and thus produce
the renal activity graph. So, we create the two masks, in the
form of logical arrays of size 128x64. In these masks, points
inside the kidney regions have a value of 1, and points outside
of these regions have a value of 0. Next, the algorithm counts
how many images are included in the exam folder (usual
number is 26 – one image taken per minute in the course of the
exam, but, depending on the needs of the doctor and the
patient, this number could vary), and then it splits each image
in the two parts already explained. The algorithm then
superimposes the masks on the actual images of the exam, one
by one, and records for each image –separately for right and
left kidney-in a new array the sum of brightness values of
pixels inside the areas marked with 1 in the masks. Now, we
have two 1xn arrays (n being the number of individual images
in the exam). Let L(1,n) and R(1,N) be the two arrays,
indicating the Left and Right kidney respectively. Now L(1,i) is
the sum of the brightness values of the i-th image of the Left
kidney. The remaining step is to plot these two brightness
arrays in a graph that shows brightness vs. time for each
kidney. In Figure 8 we can see an example result of the
application of the algorithm.
Fig. 8. activity graph – (a) initial image (b) detected kidney regions (c)
activity graph
2) Renal Pelvis Detection
In this section we will analyze the code for the detection of
the ROIs belonging to the kidney’s renal pelvis. The renal
pelvis is a funnel-shaped part that connects the ureter to the
kidney. Its function is to funnel urine flowing from the kidney
to the ureter.
Fig. 9. Frontal renal section, with the pelvis depicted in the middle
The algorithm for the isolation of the renal pelvis is quite
similar to the algorithm described for the isolation of the whole
kidneys, in the previous section. The problem of detecting the
renal pelvis is quite complex and it is a very difficult part of the
organ to detect due to its uneven shape, as depicted in Figure 9.
Since the images produced by renal scintigraphy are “grainy”
and of low definition, the renal pelvis detection is an arduous
task, performed only by the doctor at the time of the exam.
Although it is very hard to discern in such small images, we
have two basic “guidelines” stemming from the actual practice
of isolating it visually, which we will try to exploit, in order to
detect it automatically: The most prominent one is its position,
at the inner edge of the kidney, and the secondary one is a very
mild fluctuation of brightness at the inner edge of the pelvis, in
the areas where the calyxes border with the papillae. This
approach combines mathematical morphology and a “game of
life”-type of approach, centered around the expected time of its
maximum brightness during the exam.
After examining the physician’s archives of true patients’
renal scintigrams and following the physicians’ guidelines, the
result was that the renal pelvis was more discernible to the
human eye, between the 12th and 15th minute of the
examination. At this point of the scintigram, the radionucleoid
is mainly concentrated on the renal pelvis, since it has began
flowing from the kidney to the ureter, as depicted in Figure 10.
Fig. 10. Showing the brightness of the renal pelvis (outlined in black) in the
12th minute of the exam
In this figure – converted to RGB color for better
discernibility- we can see the bright red spot that marks the
renal pelvis in the left kidney of the patient, and the somewhat
darker blue region in the right kidney (reversed image).
Apparently, the specific patient’s right kidney is dysfunctional,
while the left may be over-active. This algorithm follows the
same steps with the previous one, concerning the steps up to
the boundary tracing of the kidneys.
a) Creating a wide bounding box around the ROI
Our task after that, is to create a wide ROI around the renal
pelvises, and shrink it incrementally until we have obtained a
more-or-less satisfactory boundary for the pelvis itself. Thus
our first concern is to build a Bounding Box around the wider
area of the pelvis, based on the data of the boundary tracing
algorithm and the spatial properties of the pelvis (inner part of
the kidney).
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The first rough outline of the pelvis region is now
calculated, as depicted in Figure 11.
Fig. 11. Rough Outlines of the Renal Pelvis ROI’s as acquired after
Boundary Tracing
b) Detect Pelvis exploiting statistical measures
Since we have roughly defined our initial ROI’s for the
renal pelvis, we can now begin tightening our knot around the
area, exploiting brightness and its statistical measures. Our aim
here is to produce two new images (one for each kidney) each
of which will have its maximum brightness value inside the
Renal Pelvis, and which will be used to “build” an as-close-as-
we-can approximate outline of it.
More specifically we exploit the mean, median and
maximum value of the brightness inside the Bounding Box,
“evicting” from it pixels with brightness less than a certain
threshold, specific for each exam, but greater than the 75% of
the maximum brightness value. In this case too, it has been
experimentally observed that a good rule of thumb is to initially
exclude pixels with brightness less than 120-130, but this posed
inaccuracies in exams with low overall brightness, so the use of
the mean and maximum value was necessary to adapt the
algorithm to each individual exam.
c) Applying Mathematical Morphology to ROI’s
This is the most crucial part in the procedure of Renal
Pelvis Detection. As we previously discussed, the shape of the
renal pelvis is quite irregular and could not be described by the
usual elementary structural elements, used in mathematical
morphology. Nevertheless, beginning from a small core of
pixels with adequate brightness and at the right position, we
can create a shape which will be an adequate –although not
perfect- approximation of the Renal Pelvis.
Before performing our structural transformations, we have
to clear the area of any possible isolated pixels outside the
Bounding Box we have created, so one more time we perform
mathematical cleaning operations in both images.
After our image is cleared from isolated pixels, we now
calculate the dimensions of the Bounding Box, and following,
we create the structural elements of “line” and “rectangle”:
• The “line” structural element is defined by its length and
its angle to the horizontal axis. If our Bounding Box is
larger than 10 pixels in length, then the length of the
“line” structural element is defined as :
]
2
)(
[
xBoundingBoLength
l = ,
where [ ] denotes the integral part.
• If the Bounding Box is smaller than 10 pixels in length,
then the “line” structural element has length equal to it.
• The angle of the structural element was experimentally
calculated, to be 55O for the right kidney and 125O for
the left kidney, so the structural element approximates
the angle of the kidneys in the scintigram (relevant to the
horizontal axis).
• The “rectangle” structural element has always
dimensions of 2x1 pixels. It is adequately small to create
an initial centre of reference for the mask to be extracted
later.
• Having created the structural elements in the brightest
spot of our bounding box, we recursively perform the
procedure of mathematical erosion already described, in
order to acquire an area approximating the area of the
Renal Pelvis.
Fig. 12. Approximation of the renal pelvis area, using mathematical
morphology (structural elements and erosion)
d) Completing the procedure
After having procured an approximation of the Renal Pelvis
ROI, we follow the steps for mask creation described in
previously, with the addition of a step of conversion of the
image to RGB colour for a better visual clarity of the final
image. In Figure 13 the results of the application of the
algorithm in two separate patient files are shown.
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Fig. 13. : Results of Renal Pelvis Detection algorithm – (a) initial image,
(b) image with renal pelvis areas in blue, (c) renal pelvis activity graph
The algorithm produced adequate results in more than 80% of
the tested samples of the physician’s patients’ archives.
Although the algorithm is not perfect and there are
imperfections in the final results of the produced activity
graphs, this first implementation shows promising results and
will be further exploited to produce a first Computer Aided
Diagnosis tool, to help physicians in their initial evaluation of
the patients’ health.
IV. TEST AND EVALUATION
The algorithm was implemented exclusively in MATLAB
7.3. Our sample consisted of more than eight hundred (800)
scintigrams, taken in a private clinic in a span of 5 years. The
scintigrams were taken from adult patients of various ages and
both sexes, both healthy and suffering from various conditions.
The scintigrams were produced with GE’s 3000 γ camera
attached to a STARCAM computer, each exam consisting of
26 images, one per minute. The patients were injected with
weight adjusted doses of 99MTc-MAG3 before the exam and
LASIX during it, to evaluate excretion rate. The inputs of the
algorithm consisted solely of the kidney images produced by
the STARCAM and no other evaluative input was used. The
images were in PNG format.
We evaluated the results of our algorithm in comparison to
the results from the STARCAM computer. The main advantage
of our algorithm was the removal of irrelevant to the kidneys
areas outside of them. In most cases, the STARCAM computer
did successfully trace the boundaries of the kidneys, but
included in this trace bright areas outside the kidneys, owed to
artifacts and image noise. With the present algorithm we had a
success rate of more than 90% in successfully detecting the
correct areas of the kidneys and more than 95% success at
removing artifacts, thus resulting in a better production of the
renal activity graph and aiding the doctor in a better diagnosis.
In Figure 14 we can see a comparison between the detection of
the kidneys by the STARCAM, on the left, and our algorithm,
on the right. On the left there are inclusions of areas outside the
kidneys, but on the right, after processing of the image, the
algorithm outputs clear outlines of the kidneys and no other
irrelevant area is included. This results in a more accurate
production of the renal activity graph. Nevertheless, the
differences in the end result of the produced renal activity
graph are either way not huge. Since, even in the case of other
areas inclusion, their total area and brightness sum are not
adequate enough to influence the original activity graph in a
way that would produce extreme misleading results in the
doctor’s evaluation. Doctors were also always taking into
account in their diagnosis this fact and thus eliminated
intuitively the small error factor imposed by such imperfections
in the original algorithm.
Fig. 14. Left – Kidney areas as detected by Starcam, Right – Kidney areas
detected by our algorithm
The bigger innovation of this algorithm though, lies in the
detection and production of the activity graph for the renal
pelvis area. The renal pelvis is a very difficult area to detect
and trace, but our algorithm showed good results in more than
70% of the sampled images. Being an area that lies on the
inner-edge of the kidney, but also with an “inconsistent” shape,
and due to the numerous conditions that can be affecting it, it is
deemed extremely hard to automatically detect it with extreme
accuracy. Thus, the initial idea of the doctors’ requirements
was to produce an algorithm that would let them manually pick
the area and then produce the activity graph. But since the
initial algorithm performed extremely well with the whole
kidneys, the idea of auto-detecting the renal pelvis with a
promising success ratio was not deemed to be impossible. The
production of the activity graph for the renal pelvis was judged
by the evaluating physicians to be of big help in their
diagnostic procedure, although of course, since the accuracy of
the algorithm results varies, it was used with prudence,
depending on the judgement of the doctor.
V. CONCLUSIONS
The application of the algorithm in numerous cases
produced satisfactory results. The main algorithm showed great
promise in accurate detection of the kidneys and production of
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the renal activity graph. The renal pelvis detection algorithm
provided us with satisfactory first results in many cases, but
showed weaknesses in some cases, by either over-covering the
area of the pelvis or detecting smaller parts of it. These
weaknesses were owed to brightness fluctuations in the area,
exceeding the limits posed by the algorithm, which leads us to
the conclusion that the algorithm needs improvement in its
adaptive part, to cope for extreme conditions. Thus, both
algorithms can be improved, aiming at two distinct goals. The
first one being to further improve the renal pelvis detection
function in order to have more accurate trace of it and thus
facilitate the production of better activity graphs for it. The
second goal is to evaluate modified applications of the
algorithm in different kinds of medical imaging data, sourcing
not only from radio-nuclear-generated imagery but from other
sources as well.
It is very important to note that the purpose of the
abovementioned study is not to provide a diagnosis replacing
the physician but rather to provide more accurate results so as
to assist him in the interpretation of findings.
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