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International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 2. Issue 10 October 2013 Page No. 3075-3080
Automatic Land Marking on Lateral Cephalogram
Using Single Fixed View Appearance Model for
Gender Identification
Janaki Sivakumar1, Prof.K.Thangavel2
1
Research Scholar, Prist University
Tanjore, Tamilnadu
2
Head Of Computer Science, Periyar University
Salem, Tamilnadu
Abstract: This manuscript describes Active Appearance model for automatic Cephalometric analysis in forensic science, which is
concerned with the recognition, identification, individualization, and evaluation of physical evidence. A complete model of shape and
texture was built from a dataset of manually annotated images, and then tested with unseen images. To apply AAM Method for
identifying landmark points 140 images were collected from AAOF out of it 67 were female and 73 were male with age ranging from 15
years to 45 years. By using the AAM algorithm, the mean shape is extracted and the appearance variation collected by establishing a
piece-wise affine warp between each image of the training set and the mean shape.
Keywords: Active Appearance Model, Forensic Science, Cephalometric Analysis.
1. Introduction
Usually Forensic science is concerned with the recognition,
identification, individualization, and evaluation of physical
evidence. Computational forensics is the study and application
in forensic investigations. This involves the use of computer
models and simulations, applying mathematical techniques to
quantify evidence and probabilities. Computational forensics is
particularly useful in identification of criminal and evidential
patterns. Forensic scientists present the findings from
Computational forensics,as an expert witness in the court of
law.
Cephalometry is the study of human head in terms of skull. The
dimensions are measured based on Lateral skull X ray views
which are called cephalograms.Analysis of feature point
extarctions from cephalograms is called Land mark
identification in Hard and Soft tissue.Orthodontics uses soft
tissue landmarks like LabraleSuperius, LabraleInferius,
SoftTissuePogonion and Pronasale, Whereas computational
forensics needs hard tissue land marks like Sella,Nasion
,Porion ,Orbitale ,A Point ,B Point Menton and Gonion for
crime investigationin the aspects of gender discrimination,age
and race. Manual cephalometric annotation had been used
widely. It can take an experienced orthodontist up to thirty
minutes to analyze one X-ray. Automation of cephalometric
analysis will reduce the analysis time and improve the
landmark identification.
3.1 Landmark identification from CR –cephalograms
Manual Method: the oldest and still most widely used method
is analyzing CR- Cephalometric images and manually tracing
skeletal features identifying feature points , and measuring
distances and angles between landmark locations.
Drawbacks: In manual approach, errors vary significantly
based on feature points, expert who is tracing the landmark
points and the quality of the CR-cephalograms. Alsothis is
monotonous, time-consuming and error prone approach.
Automatic Method: There are various approaches (Table
1)to detect feature points automatically that takes the
differences of characteristics in the images to detect feature
points. Automatic approaches helps in minimizing time, more
accuracy and reducing errors. General standard approach
forland mark identificationmeasurements with errors are:
landmarks should be located within 1mm tolerance; although
2mm is deemed acceptable for clinical practice
Attempts for automatic cephalometric analysis began from
1985 by Hussain et al. [4]. Levy Mandel et al. [5] used line
extraction to find the landmarks of these lines. Searches for
specific lines in a specified order taking into account the
segments that constitute the line and intersection between lines.
Parthsarathy et al. [6] used feature recognition techniques to
locate the landmarks. A resolution pyramid of three levels was
first created. The search begins in course level then moves to
Janaki Sivakumar,1IJECS Volume 2. Issue 10 October 2013 Page No.3075-3080
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high resolution to reduce the time. Five cephalograms (480 X
512) with 256 grey levels had been searched for 9 landmarks.
The error exceeded 3 mm on average. An interesting table for
the difference between the experts themselves had been
presented.
It has been suggested that an error of 2 mm in landmark is
acceptable [7]. But an accuracy of 1 mm is desirable [8].
Forsyth and Davis [8] used ten cephalograms scanned at 512 X
512 pixels with 64 grey levels. On average 63 percent of 19
landmarks were located within 1mm and 74 percent to within 2
mm.
The conjunction between image processing techniques and
prior knowledge of the cranial structure had been used in all
these earlier works. These techniques were limited to small
dataset of images. So the underlying flaw is that they had to
handle variation in biological shapes and quality in coding the
algorithm.
A learning approach for automatic landmarking gave more
accurate results than the earlier handcrafted algorithms.
Cardilllo and Sid -Ahmed [9] located 76% of 20 landmark with
2 mm in a training set of 40 images (512 X 480) pixel with 256
grey levels. They used sub-image matching on hand selected
features. Hutton,T.J et al. [10] used Active Shape Model
(ASM) on a large dataset. Average error of 4mm of 16
landmarks was achieved. Chakrburthy et al. [11] used support
vector machines. 70 image (760 X 500) for training and 40
images for testing. 8 landmarks were used. Accuracy of 95%
can be achieved for most landmarks given a larger tolerance
value of 5 mm. Innes et al. [12] used pulse coupled Neural
Network (PCNN). The output of PCNN was used by a
subsequent process such as curve following of Hough
transform to identify the positions cephalometric landmarks.
109 images had been used with accuracy of 93.66% one
landmark on soft tissue, 36.6% of 2 landmarks on bony
structure. Automation of finding parameter values of PCNN
processing will be useful for the different landmarks.
Romaniuk et al. [13] used non-Linear Principle Component
Analysis with Average Mean of 3.3 mm. Recently, Ciesielsk et
al [14] used Genetic programming for four landmarks using
100 images.
 Texture involves intensity of an image in a normalized
frameupto some offset values.
To apply AAM Method for identifying landmark points, 140
images were collected from AAOF collection out of it 67 were
female and 73 were male with age ranging from 15 years to 45
years.AAM single view –drop out method is implemented by
using Matlab.Each image has a sizeof 780×690 pixels and 256
grey-levels. 51 land marks were considered out of these 51
points 8 points are primary cephalometric landmarks and the
remaining 43 points were considered as auxiliary points. These
cephalograms were scanned in using a Microtek Scan Maker 4
flatbed scanner at 100 dpi ( 1 pixel = 0.25 mm).
All the images were padding with black valued pixels from the
right and the bottom to make all the images have the same size
(794 X 1042) with 256 grey levels which is crucial for AAM to
work. Twenty images were used for building the model and
seven images were used for testing the model. This small data
set was used for the comparison between the two techniques
and testing the whole set will be achieved later.et:
3. Structure of Appearance Model
Principal component analysis is used to find the mean shape
and main variations of the training data to the mean shape.
After finding the Shape Model, all training data objects are
deformed to the main shape, and the pixels converted to
vectors. Then PCA is used to find the mean appearance
(intensities), and variances of the appearance in the training set.
Both the Shape and Appearance Model are combined with
PCA to one AAM-model.
By displacing the parameters in the training set with a know
amount, an model can be created which gives the optimal
parameter update for a certain difference in model-intensities
and normal image intensities. This model is used in the search
stage.
Active Appearance Model (AAM) was described by Cootes
et .al [15]. A direct extension of the ASM approach used
in[10]. Besides shape information the textual information (i.e.
the pixel intensities across the object) is included into the
model. The aim of this study was to evaluate usage of AAM
followed by simulated Annealing for automatic cephalometric
analysis.
Figure 1: An Appearance Model Structure
2. Active Appearance Model
AAM is an hybrid approach which involves Average
shape(Model Based Approach) and training set(Soft
Computing Approach). Here the term ‘Appearance’ includes
both shape and texture.
 Shape is a metric which denotes characteristic location
from the image with some allowable basic
transformations like rotation,scaling andtranslation.
According to shape definition by Kendal [16], shape is
invariant to Euclidean transformations (i.e. translation, scaling
and rotation need to be filtered out). All the samples were
aligned to the mean shape iteratively described by Goodall
[17].Principal Component Analysis (PCA) was performed after
alignment of all shapes to the mean shape. PCA was used asa
dimensionality reduction technique by producing a projection
of a set of multivariate samples into a subspace constrained to
Janaki Sivakumar,1IJECS Volume 2. Issue 10 October 2013 Page No.3075-3080
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explain a certain amount of the variation in the original
samples.
The method represents landmark points, (xi, yi), for each
image as a 2n vector, x, where x = (x1, . . . ,xn, y1, . . . ,
yn)Twill describe the shape of an object. The method performs
a Principal Component Analysis (PCA) on the aligned training
set to describe the shape and appearance variations (texture) of
the object.
Then apply PCA in training set as follows:
Model of the shape is performed as
Figure 2: System Architecture
Where,
s
Es
Vs
– is the mean
– Eigen Vector Matrix
– Model Vector
Model of the Texture (Gray) is performed as
To apply AAM Method for identifying landmark points, 140
images were collected from AAOF collection out of it 67 were
female and 73 were male with age ranging from 15 years to 45
years.AAM single view –drop out method is implemented by
using Matlab.Each image has a sizeof 780×690 pixels and 256
grey-levels. 51 land marks were considered out of these 51
points 8 points are primary cephalometric landmarks and the
remaining 43 points were considered as auxiliary points. Below
are the primary cephalomatric landmarks to identify gender.
Model and texture parameter variance are determined by the
Eigenvalues
Often, shape and texture are correlated. For combining shape
and texture, joint parameter vector bi is used in every pair
(xi,yi) of the training set as
Where
W
st
is a suitable Weight matrix between the pixel
distances (shape)
and pixel intensities (texture)
Apply PCA to the training set
Where
Finally the combined model is represented by combined
parameter C
Where
Figure 3: Cephalometric Landmarks
3.1 Method to Identify Reference points
 Sella (S)—the center of the hypophyseal fossa (sella tursica).
 Nasion (Na)—the junction of the nasal and frontal bones at
the most posterior point on the curvature of the bridge of
the nose.
 A-point (A)—an arbitrary measure point on the innermost
curvature from the maxillary anterior nasal spine to the
crest of the maxillary alveolar process. A-point is the most
anterior point of the maxillary apical base.
 B-point (B)— An arbitrary measure point on the anterior
bony curvature of the mandible. B point is the innermost
curvature from chin to alveolar junction.
 Menton (Me)—the lowest point on the symphysis of the
mandible.
 Anterior Nasal Spine (ANS)—the most anterior point on the
maxilla at the nasal base.
 Orbitale (Or)—a point midway between the lowest point on
the inferior margin of the two orbits.
Janaki Sivakumar,1IJECS Volume 2. Issue 10 October 2013 Page No.3075-3080
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 Gonion—a point midway between the points representing
the middle of the curvature at the left and right angles of the
mandible.
 Articulare (Ar)—a point midway between the two posterior
borders of the left and right mandibular rami at the
intersection with the basilar portion of the occipital bone.
 Porion (Po)—the midpoint of th upper contour of the
external auditory canal (Anatomic Porion) or a point
midway between the top of the image of the left and right
ear-rods of the cephalostat (Machine Porion).
Figure 2: Landmarks -51 points
Step 2: After the training set is made ready that will be used
(MATModel file) in ModelAAM to create average shape and
Texture (Figure 1 An Appearance Instance & Figure 3 : Mean
Shape)
Figure 4: An Appearance Instance
For applying AAM methodology, the well known scheme
called drop out method was used. This approach uses M-N
images for training and N images are used to test the
performance of AAM. In this approach, three sets of enhanced
images were used for AAM Approach
 Mean Filtered image Data Set: Out of 67 female and 73 male
data sets, first 20 images are used for training purpose on
each.
 Weiner Filtered Image Data set: Out of 67 female and 73
male data sets, middle 20 images are used for training
purpose on each.
 GaussianFiltered Image Data set: Out of 67 female and 73
male data sets, last 20 images are used for training purpose
on each.
Figure 3: Mean Shape
Step 3: Fit GUI use each image in the image dataset to compare
with trained set and automatically mark 51 points that will be
stored in .ASF file as wordpad file(Figure 4 Input mask)
By using the above explained AAM algorithm, the mean shape
is extracted and the appearance variation collected by
establishing a piece-wise affine warp (based on the Delaunay
triangulation) between each image of the training set and the
mean shape.
3.2 Method implementation
Step1: First By using AnnotateGUI, annotate 51 points in each
image of the training setMale 15, Female 15(Figure 2
Landmarks -51 points)
Figure 5: Input mask
4. Conclusion
Automatic Cephalometric analysis becomes an important issue
for time saving and robust landmarks identification. AAM
provides a compact model, which takes advantage of all the
information available by landmark locations and pixel
Janaki Sivakumar,1IJECS Volume 2. Issue 10 October 2013 Page No.3075-3080
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intensities. The results show that AAM followed by PCM can
give more accurate results than ASM used by Hutton, T.J [7]
because the same dataset had been studied. But due to the large
memory requirement of AAM, we didn’t use all of the
available 140 images. Further we can incorporate a
compression technique such as wavelets for modeling the
texture variations in AAM as suggested by Stegmann et al.
[18]. From the nature of multivariate linear Regression, It can
be seen that a large number of experiments during the building
model constitutes a better predication model so if we increase
the number of training images we can get more accurate results.
To improve the efficiency and robustness of the algorithm, it
can be implemented in a multi-resolution framework. Searching
begins in a coarse image then refining the location in a series of
finer resolution images. This will lead to a faster searching and
less likely to get stuck on the wrong image structure.rentheses.
[10]
[11]
[12]
[13]
[14]
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