Aveneu Park, Starling, Australia

DENTAL that the information content of the image is



Rameswari Poornima Janardanan

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Abstract — Image
processing is a strong tool aiding medical and forensic research . A systematic
review is done in this paper in the area of dental image analysis applied to
forensic odontology using dental x-rays. The interpretations of medical images
rely hugely on human involvement and the human perception of the details
present in it.  The interpretation of the
delicate fine details in various contrast situations present in a medical image
is indeed a cumbersome task to assess. Typical radiographs obtained from a
regular radiograph acquisition device may be of average quality in
representation. Various standardized scientific tools have been designed by
researchers, scholars and software developers to address this type of
shortcomings in a medical radiograph. These are targeted to minimize the
possible human error in predicting the right diagnosis and treatment solely on
the basis the human visual perception. Feature extraction by teeth segmentation
on focused area for the information required; on an extracted tooth area in a
digital dental radiograph is highlighted in this review.

1.  Introduction

Image processing includes
several methods like enhancement, segmentation, filtering methods, thresholding
technique and morphological operations. The information from texture, shape,
contours, etc is used in the classical image segmentation. Edge detection is
used to find boundaries of objects inside an image. Image enhancement
techniques are used to restore the original image.

              Medical imaging technology has
revolutionized the health care over the past three

 decades, aiding doctors to diagnose and
improve patient outcomes. A
fight against cancer is fought effectively using medical imaging in its
prevention, diagnosis and, treatment. An important advantage of digital dental
radiography is its ability to process the image data, so that the information
content of the image is more accessible to the human visual system. Dental
professionals today are increasingly using digital dental x-rays for better
detection, diagnosis, treatment and monitoring of oral conditions and diseases.
Traditional x-ray films are replaced by the digital electronic sensors. These
sensors can produce enhanced computer images of intra oral structures and

               The aim of this systematic
review is to give an overview towards current dental image processing methods
used in forensic odontology because of their potential importance in the dental
and forensic fields.

this paper is sectioned and sub sectioned as section 3.1 Reviews various
techniques used for image segmentation and feature extraction on dental
radiographs. Section 3.3reviews various techniques used for image
segmentation and feature extraction on dental radiographs. This section also
highlights the works done in forensic odontology using image segmentation.
Section 3 concludes the present review.

1.1 Why it is important to do this review

The relevance of this review is grounded on the
need to recommend a method for dental age estimation and human identification
with the following characteristics: simple, fast, non-invasive, non-expensive,
reproducible and over all, accurate, that can be systematically used in
different academic and forensic scenarios. This efficiently assists in identifying deceased individuals or
identify human profiles in any doubtful situations


2.   Methodology

There had been many trials to
develop an automated computer vision based system to facilitate forensic
odonatological applications. These systems comprises of variety of image
processing techniques.  The basic
algorithms and methods used in dental x-ray processing are image enhancement,
image segmentation, edge detection with feature extraction and neural networks
based classification.


2.1 Eligibility criteria for
considering in this review

The scope of this review was not
limited to general dental image processing methods, but a brief description of
its clinical and forensic applications were reviewed. The inclusion criteria were original studies with dental image processing
techniques with forensic applications. The
eligibility criteria are as shown in the Table 1.


Table 1. The eligibility criteria for considering in
this review.


Eligibility criteria


with different methodology

of the algorithm

Low failure rates



Table 2. Summary of included image
processing methods used on dental radiographs and its purpose.



Image processing methods used

Purpose of the study/Application

Year of study

Nomir, Mohamed Abdel-Mottaleb

two-stage segmentation method is used. First to separate teeth from
background and the second separates upper jaw from lower jaw using integral

human identification from X-ray dental radiographs


Eyad Haj Said, Diaa Eldin M. Nassar, Gamal Fahmyand Hany H. Ammar

Teeth segmentation using a mathematical-
morphology (MM) approach.

For developing automated dental identification


A.K. Jain and H.

Using active contour extraction model (ACM)

For matching dental x-rays for human identification


R. Cameriere, S.DeLuca, N.Egidi, M.Bacaloni,
P.Maponi, L.Ferrante, M.Cingolani.

Automatic age
estimation in adults by analysis of canine pulp/tooth ratio: Preliminary

To assess
dental age from peri-apical x-rays


G., Tuceryan, M. & Blitzer.

the tooth contour from a bite mark image and compare it  to each contour from a dental model by
finding the ideal alignment and calculating goodness-of-fit.

bite mark identification




 2.2 Exclusion criteria

Studies which
had similar methods and often used were excluded. Non English papers were not
considered in this review


 Table 2. The list of data that was extracted
from the reviewed full texts.

Data extracted from full text items

First author and Year


Segmentation method

Matching technique

Purpose of the study/Application

Performance rate


2.1 Study identification and

The information was searched through
the data base available through the Saudi digital library accessed through the
e-library facilitated by Riyadh Colleges of Dentistry and Pharmacy. Directory
of open access journals(DOAJ), Medline/PubMed (NLM), ProQuest, Collection, (Web
of science), Science Direct Journals(Elsevier), Wiley(Cross Ref),Wiley Online
library, google scholar were accessed to assimilate information  this review. Reviews, articles, reports and
original papers published in peer journals, books, conference proceedings for
grey literature were all considered. English language publications from any
setting and recent time frame from 2010 till date, were considered eligible.

The search keywords used were dental
image processing, image segmentation on dental radiographs, human
identification from dental x-rays, dental age estimation methods


extraction and management

The collected information was organized in an
excel spread- sheet as follows: Author, year,
title, enhancement technique, segmentation technique, feature extraction
method, matching method , performance/accuracy and its applications.


2.3 Assessment of risk of bias in included

It was necessary to avoid
bias in this systematic review and thus to be free from a false positive appraisal
or a false negative conclusion. The possibility of author bias was analyzed and
asked for the participation of the same authors in repeated publications.  The individual papers were analyzed by
comparing and then grouping them per author.



Fig. 1. Flow
chart of the study selection this review


3.   Review of image enhancement
methods used on dental X-ray images

In the process of digital radiography, an
electronic senor is used to capture images of the oral cavity and its
structures in place of traditional films. This once connected to a computer the
image can be viewed by a dentist on a screen of choice. Digital images are the
most crucial medium in the field of computer vision. A digital radiograph has
the advantage of immediate image preview and availability, and eliminates the
cost of film processing steps. It provides the ability to apply special image
processing techniques that enhance the overall display quality of the image and
extract only the regions of interests.  With
image segmentation on digital dental radiographs, the exact information of the
region of interest can be extracted. This information is an important tool in
clinical, forensic and therapeutic applications in the field of dentistry.


3.1 Reviews various techniques used
for image segmentation and feature extraction on dental radiographs

review on dental biometric systems and technology with further applications in
forensic science was done .


2 Nomir and Adlab-Mottaleb presents a system in which, given a dental image
of a post-mortem (PM), the proposed system retrieves the best matches from an
ante mortem (AM) database. The system automatically segments dental X-ray
images into individual teeth and extracts the contour of each tooth. Features
are extracted  from each tooth and are
used for retrieval. During retrieval, the AM radiographs that have signatures
closer to the PM are found and presented to the user. Matching scores are
generated based on the distance between the signature vectors of AM and PM teeth.


5. Block diagram of segmentation algorithm. (Omaima Nomir ,2005)


introduced iterative and adaptive thresholding. Thereafter horizontal and
vertical integral projection is used for separating the jaws as well as
individual tooth. The block diagram of segmentation algorithm is as shown in
Figure 5.This technique was not successful in matching images due to poor
quality of images and shape of teeth could have changed with time as PM images
were taken after a long time AM images were captured.


In 3, Eyad Haj Said, Diaa Eldin M Nassar, Gamal Fabry & Hany
Ammar presented method of teeth a mathematical
morphology approach to the problem of teeth segmentation. They also proposed a
grayscale contrast stretching transformation to improve the performance of
teeth segmentation. We The various techniques for dental segmentation of X- ray images
address the problem of identifying each individual tooth and how the contours
of each tooth are extracted is presented. Their technique was not able to
properly segment an X- ray by a single segmentation technique and it varied
from image to image.

 (Said,E.H,2006) in his paper designed an
approach based on  mathematical
morphological segmentation. Greyscale contrast stretching transformation is
performed for an enhanced teeth segmentation performance. It presented a
technique with a low failure rate on comparison to other approaches.


Figure 6 Main
stages of the algorithm(Said,E.H,2006)

Figure . 7
Grayscale line profiles of the input image, the upper horizontal line profile
illustrates the bones between the teeth, the lower horizontal line profile
shows the gap between the teeth, while the vertical line profile illustrates
the gap valley. (Said,E.H,2006)


5, Hong Chen & A.K. Jain introduced dental biometrics using active
contour extraction model (ACM). As per this paper traditional snake cannot able
to discriminate edges of multiple adjacent objects. So there can be presence of
overlapping images. To remove this problem the authors utilized direction
gradients. This proposed system has main two stages: feature extraction,
matching. In this to extract contours of dental work the intensity histogram of
the tooth image is automated with the mixture of Gaussian model. In the
matching stage three steps given: Tooth level matching, tooth contours are
matched using a shape registration method, and the dental work is matched on
overlapping areas. Distance between postmortem and ante mortem radiographs
provide candidates identities to estimate subject identification. The tooth
contour is the feature extracted as they remain invariant over time in
comparison to other feature of the teeth. Radiograph segmentation and contour
extraction are done in the feature extraction stage. Based on edge detection
contour extraction is approached.


Figure 9 .The
processing flow diagram(Chen and Jain 2005) and
the results of teeth alignment and dental work alignment with the parameters
used in teeth alignment. (a) Query DW. (b) Genuine DW. (c) Imposter DW. (d) The
contours of the DW in (b) and the DW in (a) being affine transformed with the
teeth alignment parameters between (a) and (b). (e) The contours of the DW in
(c) and the DW in (a) being affine transformed with the teeth alignment
parameters between (a) and (c). (Chen and Jain 2005)



In their paper R. Cameriere, S.DeLuca  in 2015 proposed for the first time automating
teeth segmentation for the purpose of dental age estimation based on a previously
proposed formula. Here the segmentation is done in two steps for the tooth and
the pulp.

As the intensity of the pixels is greater in the tooth than in the
background, a suitable threshold is selected. From the knowledge of set T, a
piecewise linear approximation of its boundary is computed. This is an
important item of information in the segmentation of the pulp. In fact, the
boundary of the tooth encloses the region where the pixels of the pulp area can
be found.

Shape analysis is applied to all the transversal sections of the
tooth. The shape analysis of the transversal sections is based on the
characteristic M-shape of the corresponding grey level function. In every
transverse section, the arithmetic mean of the grey levels of pixels around the
middle of the pulp area are compared to the arithmetic mean of the grey levels
of the pixels around the boundary of the pulp area. When the absolute value of
their difference is lower than a given threshold it is the pulp end. Finally, a
piecewise linear approximation of the points of the pulp boundary is obtained
by a least square polyline of these points




Flora, G., Tuceryan  in 2009 proposed a method for bite mark
identification by extracting tooth contour for matching. The general steps for
bite mark identification are as follows: 1. create a digitization of the set of
3-dimensional dental casts. 2. capture a 2-dimensional contour of the teeth
from each digitization. 3. capture the tooth contour from each bite mark image.
4. compare each bite mark image contour to each contour from the dental model
by finding the ideal alignment and calculating goodness-of-fit. 5. the
comparison which causes the maximum goodness of-fit is identified as the match



Fig: 11.
Typical captured tooth contours using the deformable curve. Fig 12.

Fig 12. Matching results
for the comparison using bite mark contours extracted with deformable curves.


Table 3. A
comparison between the teeth segmentation algorithms




of view

it automated

and Cheng




&Abel Motleb

&adaptive Thresholding, integral projection



E .H. Said, D. Nassar

morphology of teeth

wing and periapical


R. Cameriere,  &S.DeLuca,

technique and shape analysis



G.&Tuceryan, M.


2-dimensional contour of the teeth from 3-dimensional dental casts



In teeth segmentation algorithms optimality and
percentage of failure shows light on performance of segmentation algorithms
Measuring suboptimality measures the performance of algorithms in between the
two extremes. In practical cases, it is difficult to achieve optimal
performance with 100% images, and when comparing segmentation algorithms, one
should favor those whose failure rates are the lowest and their optimality and
low-order measures of suboptimality predominate the testing results 16. The
failure rate is especially important when assessing teeth segmentation
algorithms, since those films where no teeth can be properly segmented cannot
be used in the identification process.

Results and conclusion

For feature  extraction and
segmentation most research scholars makes use of thresholding and morphological
operation. From the review of above
papers, the main challenge in developing an automated dental recognizetion
system is to deal with poor quality of images, imaging angle, teeth overlap,
teeth shape change matter due to aging, occluded teeth, etc. Incorporating artificial
intelligence(AI) tools such as neural
networking, fuzzy c-means, etc
has not been much explored, for the better understanding and diagnosis purpose.
Researchers up till now have been
found concentrating on image enhancement or segmentation for
extracting features for forensic sciences for human identification. No deep
research has been published for automating dental atlas for human age
prediction for odonatological purposes. Bite mark analysis using image
processing is still not well explored. Automated or semi-automated diagnosis of aforesaid objectives would be quite useful for further identification of
human and especially for asylum seeker issues. This systematic review summarizes and compares the results of some of the most used methods for dental image
segmentation methods used in forensic application mainly for human
identification. In the light of the evidence one could identify there is a need
to identify a teeth segmentation algorithm with a better performance rate for
further forensic and clinical applications


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