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1. INDIAN DENTAL ACADEMY
Leader in continuing dental education
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2.
literature review focused on a single
question which tries to
identify, appraise, select and synthesize all
high quality research evidence relevant to
that question
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3.
Highest level of medical evidence
An understanding of systematic reviews
and how to implement them in practice is
becoming mandatory for all professionals
involved in the delivery of health care.
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4.
Broadbent & Hofrath – cephalometer 1931
contributed to the analysis of malocclusion
standardized diagnostic method in
orthodontic practice and research
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5.
Two approaches
a manual approach
a computer- aided approach
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6.
uses manual identification of landmarks,
based either on an overlay tracing of the
radiograph to identify anatomical or
constructed points followed by the transfer of the
tracing to a digitizer linked to a computer, or
a direct digitization of the lateral skull
radiograph using a digitizer linked to a computer
and then locating landmarks on the monitor.
the computer software completes the
cephalometric analysis automatically
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7. Landmarks digitized directly
from patient. – the DIGIGRAPH
JCO Volume 1990 Jun(360 - 367): The DigiGraph Work Station Part 1
Basic Concepts - SPIRO J. CHACONAS, DDS, MS; GARY A.
ENGEL, AB, MS; ANTHONY A. GIANELLY, DM
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8.
Cohen 1984
a scanned or digital cephalometric
radiograph is stored in the computer and
loaded by the software.
The software then automatically locates
the landmarks and performs the
measurements for cephalometric analysis
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12. Resolution pyramid
Pyramid or 'pyramid representation' is a
type of multi-scale signal representation
developed by the computer vision, image
processing and signal processing
communities, in which a signal or an image
is subject to repeated smoothing and
subsampling
Historically, pyramid representation is a
predecessor to scale space representation
and multiresolution analysis
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13. Edge detection
in image processing and computer
vision, particularly in within the areas of
feature detection and feature
extraction, to refer to algorithms which
aim at identifying points in a digital
image at which the image brightness
changes sharply or more formally has
discontinuites
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14.
In computing, a grayscale or greyscale
digital image is an image in which the
value of each pixel is a single
sample, that is, it carries the full (and
only) information about its intensity.
Images of this sort are composed
exclusively of shades of neutral
gray, varying from black at the weakest
intensity to white at the strongest.
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15. Advantages
Easy to implement
Image filtering techniques are well studied
and a large number are available
By encoding proper anatomical knowledge
better accuracy is achievable
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16. Disadvantages
Can fail to capture morphological variability
in the radiographs
Filtering results are highly dependent on
image quality and intensity level
Sensitive to noise in the image
Not all landmarks lie on edge
and, moreover, the edges or curves are
often unclear
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18. Pattern matching
In computer science, pattern matching is
the act of checking for the presence of the
constituents of a given pattern. In contrast
to pattern recognition, the pattern is rigidly
specified. Such a pattern concerns
conventionally either sequences or tree
structures. Pattern matching is used to test
whether things have a desired structure, to
find relevant structure, to retrieve the
aligning parts, and to substitute the
matching part with something else
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19. Active Shape Models
(ASMs)
are statistical models of the shape of
objects which iteratively deform to fit to
an example of the object in a new
image. The shapes are constrained by
the PDM (Point Distribution Model)
Statistical Shape Model to vary only in
ways seen in a training set of labelled
examples. The shape of an object is
represented by a set of points
(controlled by the shape model)
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20.
segmentation refers to the process of
partitioning a digital image into multiple
regions (sets of pixels). The goal of
segmentation is to simplify and/or
change the representation of an image
into something that is more meaningful
and easier to analyze.[1] Image
segmentation is typically used to locate
objects and boundaries
(lines, curves, etc.) in images.
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21. Active Appearance Model
(AAM)
Computer Vision algorithm for matching a
statistical model of object shape and
appearance to a new image. They are built
during a training phase. A set of images
together with coordinates of
landmarks, that appear in all of the images
is provided by the training supervisor.
The approach is widely used for matching
and tracking faces and for Medical Image
Interpretation.
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22. Advantages
Is invariant to scale, rotation, and
translation (the structure can be located
even if it is smaller or bigger than the given
model)
Accommodates shape variability
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23. Disadvantages
Needs models that must be created by
averaging the variations in shape of each
anatomical structure in a given set of
radiographs
Model deformation must be constrained and is
not always precise
Cannot be applied to partially hidden regions
Sensitive to noise in the image
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25. Pulse-coupled networks or Pulse-Coupled
Neural Networks (PCNNs) are neural
models proposed by modeling a cat’s
visual cortex and developed for highperformance biomimetic image processing.
Over the past decade, PCNNs have been
utilized for a variety of image processing
applications, including: image
segmentation, feature generation, face
extraction, motion detection, region
growing, noise reduction, and so on
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26.
'Support vector machines (SVMs)' are a set
of related supervised learning methods
used for classification and regression. They
belong to a family of generalized linear
classifiers. They can also be considered a
special case of Tikhonov regularization. A
special property of SVMs is that they
simultaneously minimize the empirical
classification error and maximize the
geometric margin; hence they are also
known as maximum margin classifiers.
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27. genetic algorithm (GA)
Search technique used in computing to
find exact or approximate solutions to
optimization and search problems.
Genetic algorithms are a particular class
of evolutionary algorithms (also known
as evolutionary computation) that use
techniques inspired by evolutionary
biology such as
inheritance, mutation, selection, and
crossover (also called recombination).
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28.
A neuro-fuzzy network is a fuzzy inference
system in the body of an artificial neural
network. Depending on the FIS type, there
are several layers that simulate the
processes involved in a fuzzy inference like
fuzzification, inference, aggregation and
defuzzification. Embedding an FIS in a
general structure of an ANN has the benefit
of using available ANN training methods to
find the parameters of a fuzzy system.
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30.
Results depend on the training set
Difficult to interpret some results
A number of network parameters, such as
topology and number of neurons, must be
determined empirically
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31.
To describe the techniques used for
automatic landmarking of cephalograms,
highlighting the strengths and
weaknesses of each one
reviewing the percentage of success in
locating each cephalometric point
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32.
The literature survey was performed by
searching
Medline
Institute of Electrical and Electronics
Engineers
ISI Web of Science Citation Index databases
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33.
The survey covered the period from
January 1966 to August 2006. Abstracts
that appeared to fulfill the initial selection
criteria were selected by consensus.
The original articles were then retrieved.
Their references were also handsearched for possible missing articles.
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34.
Report of mean error between real position
and estimated position of landmark for
each point
Data in millimeter
Articles in English
Articles published from January 1966 to
August 2006
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35.
Review articles, abstracts and letters
Data in pixel
Total mean error of the method for a large set of landmarks
Descriptive methods
Computer assisted method
Only graphic data on accuracy of landmark location
Recognition rate presented as percentage of success
Automatic measurements not landmarks
Cephalometric points not stated
Not every landmark detection is a cephalometric point
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42. this literature review……..
many studies seemed to be
methodologically unsound
inclusion criteria of patient radiographs
the number of radiographs used,
the error level to create a comparison with
the absence of any standard deviation of the
mean error
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43. marked difference in
results…
Heterogeneity in the performance of
techniques to detect the same landmark
Sella Point : Hybrid approaches >
model-based approach
can be due to the high variability of the
shape of sella
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44. marked difference in
results…
Porion, gonion and anterior nasal spine higher precision by the hybrid approach
Nasion - nearly the same
hybrid techniques –
better results,
accuracy close to the one suitable for clinical
practice
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45. Discussion
Recommended total error
x coordinate - 0.59 mm
y coordinate - 0.56 mm
Euclidian value of error should be 0.81 mm
amazing values for standard errors and
standard deviations that are far from
standard errors for landmark identification
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46.
2 mm difference between the location of
landmark, obtained by some automatic
method and that obtained by the human
operator, has been considered by most
people to be successful
4 mm distance acceptable
Conclusions drawn from the studies –
optimistic than reality
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47.
if one considers that two cephalometric
points are needed to trace a reference
plane or line, the resulting special
position of the line will be affected by the
errors of two points, not a single
one, and thus the error will be
increased.
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48.
studies presenting an agreement
between manual and computer-assisted
methods in millimeters, most consider
the Euclidian value, and do not refer to
the x-axis and
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49.
Automatic landmarking is the first and
last step in the development of a
completely automatic cephalometric
analysis.
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50.
Four broad categories
image filtering plus knowledge- based
landmark search
model-based approaches
soft-computing approaches
hybrid approaches
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51.
The systems described in the literature are
not accurate enough to allow their use for
clinical purposes as errors in landmark
detection were greater than those
expected with manual tracing
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52.
The ability to automatically identify
landmarks is fair for many landmarks, but
for routine clinical use it must be reliable
It should be emphasized that if automatic
land marking shall be used, it has to be
with respect to validity, reliability, and costs
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