DENTAL information from texture, shape, contours, etc is used



Rameswari Poornima Janardanan

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Abstract —
Early detection and diagnosis of diseases are facilitated by medical image
processing methods. It is a strong tool aiding medical research and related
clinical practices. An add on approach survey is done in this paper in the area
of medical image analysis for diagnosis of diseases in oral radiology using
dental x-rays in dentistry. 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 poor or 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 on focused area for
the information required, on an extracted tooth area in a digital dental
radiograph is highlighted in this feature of utmost support to a dentist for pre-diagnosis
at an early stage.

1.  Introduction

Image processing includes several methods like enhancement,
segmentation, region of interest detection, 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
because of their potential importance in the dental and forensic fields.

this paper is sectioned and sub sectioned as section 3.2  reviews the methodology a review of image enhancement
methods used on dental X-ray images. 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 automated computer vision based systems 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.

The inclusion criteria were the studies with image
processing techniques with application to dental clinical and forensic
applications. The eligibility criteria are as shown in the Table 1.

Table 1. Summary of included image
processing methods used on dental radiographs and its purpose.


Image processing methods used

Purpose of the study




















 Studies which had similar methods and often used
were excluded.

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

Data extracted from
full text items

First author



Segmentation method

Segmentation software

Clinical application


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 follow: Author, year,
country, number of participants, image processing algorithm used,and its


2.3 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



2.4 Assessment of risk of bias in included

To avoid bias in this systematic review, and to avoid false
positive conclusions or false
negative it was necessary to analyze the possibility of author bias. This owed
to the participation of the same authors in repeated publications. To this
end, the results
were analyzed comparing
individual papers, and then grouping them per author.



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


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

The physical
process of digital radiography is quite similar to traditional dental x-rays
that use films. A digital electronic senor is used to capture images of the
oral cavity and its structures. This is connected to a computer so that once
the x-ray is taken, the image can be projected on a screen for the dentist to
view. Dental images
are typically classified into periapical, panoramic, and bite wing dental
images, as shown in figure(1) and figure (2) 
and figure (3).Bitewing images are most preferred for dental processing.

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. 

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

Haj presented an over view about an automated dental identification system for
human identification1. This dental identification system can be
used by both law enforcement and security agencies in both forensic and
biometric identification. The various techniques for dental segmentation of X-
ray images to 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.

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

In  the paper by S.Kiattisin,2008 the authors
present a match of X-ray teeth films using image processing based on special
features of teeth. This method helps the dental doctors to match simply a pair
of teeth using the special features of the teeth films. Teeth’s pictures are
scanned and adjusted by a scanner and a computer, respectively, as well as then
they are converted into binary code and decoded to the direction code (chain
code). Chain code is a method for decoding a direction code from the binary
images based on the special features of teeth. The chain code of each picture
is compared with the statistical chain code. Therefore, the percentage of the
same chain code is approximately 90% (i.e. matching same patterns) for the
comparison of one root to one root (7 times) and two roots to two roots (7
times) while the percentage of the same chain code is reduced at relatively
below 50% (i.e. matching different patterns) for comparison of one root to two
roots (2 times). The percentage of the same chain code is approximately 90%
(i.e. matching same patterns) for the comparison of one root to one root (7
times) and two roots to two roots (7 times) while the percentage of the same
chain code is reduced at relatively below 50% (i.e. matching different
patterns) for comparison of one root to two roots (2 times).

(Maja Omanovic,2008)  the  sum of squared differences(SSD) cost function
shows the degree of similarity or overlapping between two radiographs degree of
similarity/overlap between two radiographs .This method was tested on a
database of 571 radiographs belonging to 41 distinct individuals. Figure 4
shows an overview of this process. A total of 150 identification scenarios were
taken then each single ROI was identified/extracted for comparing and
matching with the dental x- ray images.




Figure 4 ? Illustration of the identification test runn& top
three radiographs in the database ranked by the associated cost. (Maja


authors proposed a computer-aided framework for matching of dental radiographs
based on a sum of squared differences cost criterion. In their framework, the
operator would define the ROI by roughly circling the tooth of interest on a
given post-mortem radiograph. Hence, even untrained staff able to participate
in the identification efforts by roughly circles the tooth area. The system
itself then matches the selected region to radiographs found in the ante mortem
database. For all possible shifts, the best brightness and contrast adjustment
and rotation were computed, and the parameters that yielded.The lowest cost are
recorded along with the associated cost (match score). The radiographs in the
database were then ranked according to the cost, with the lowest cost
indicating the best match. This work was not tested on multiple ROI’s as well
as on different dental images.

Investigated the fundamental problems in image segmentation using traditional
segmentation techniques and proposed an improved technique for segmenting
images captured under natural environment. Due to non-uniform illumination it
is difficult to produce a significant threshold value along with lack of
difference in reflection. Since different illumination may produce different
color intensity of the object surface and thus lead to inaccurate segmented
images. The widely used traditional method for thresholding is ostu and fuzzy
c-means respectively. In this method, the authors have added a step extra after
thresholding with ostu method by converting the gray scale image into binary
& then integrating the  modified
threshold algorithm with an inversion technique. The results were analyzed
based on rand index function. By this the authors have concluded that the
images after ostu method and thresholding which were not able to get separate
and provide the required information are now being able to separate the
interest area & background easily. The ability of this technique therefore
has the potential to classify the poor images with inconsistent illumination condition.

Dental biometrics can be used in forensic science
for human identification. It utilizes
dental radiographs. This radiograph provides
information related to tooth shape,
teeth contour and relative position of neighboring teeth,
also gives shapes of dental work
like crowns, filling  & bridges etc. Dental
biometrics requires ante mortem (AM) and post mortem (PM) radiographs for
finding unidentified subject. Dental biometrics having three stages:
Pre-processing and segmentation of radiographs, contour extraction or dental
work extraction, atlas registration and matching. Segmentation can be done by
various methods. Contour or shape of teeth and dental work can be extracted.
Method or code was developed by the authors to locate teeth this is known as
dental atlas registration. Numbering to teeth from left to right of jaw and
also  differentiation between upper jaw
and lower jaw was done, which help in
the matching stage (S.Jadhav,2012)

 (Omaima Nomir ,2005) 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 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


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)


(Chen and Jain
2005) 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


(Hofer, Marana,
2007) proposed a method to perform human identification based on dental work
information. The algorithm involves 3 steps namely Segmentation, Feature
extraction, Creation of a dental code and matching.The dental code includes the
information about the upper and lower jaw position, sizes of the dental work
and the distance between two neighboring dental works. Maxillary and mandibular
teeth border is detected and then the intensity sum of all horizontal rows in
the strip is calculated. The dental work is identified by the highest


Figure10.  Cut stripe (region 1) and sum of intensities;
right valley represents lower intensity which indicates that the DW belongs to
the maxilla teeth (dental code = “U”). (Hofer, Marana, 2007)

The first
valley on the right and left site of the maximum intensity point is detected by
the algorithm. The lower intensity valley represents the mandibular and
maxillary teeth border.

Results and conclusion

The major researchers make use of thresholding and morphological
operation for feature extraction and segmentation. However, in the existing
software’s used by doctors the option of adaptive or global threshold is not
available. Hence, the benefits of these methods are not directly available.
Much of the work have been done for human identification, but very few
researchers have applied and realized the methods for diagnosis purpose. For
the diagnosis of intra oral diseases specifically the region of interest
selection, impacted 3rd molar using x ray rendering of 3-D images and other related
problems of gums and idiopathic resorption is still a missing feature in most
of the software’s. Interactive portions of X-ray selected for further
processing specifically for the purpose of
is the need of the hour as it would help
both doctor and patient to understand the problem and depth of disease. No
software is using AI tools such as neural networking, fuzzy c-means, etc. 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. No much research has effectively
contributed for the diagnostic methods. Automated or semi-automated diagnosis of aforesaid objectives would be quiet useful for doctor as well as
patient. Image processing & enhancement
functions are rarely
incorporated in commercial software for direct digital
imaging in dental radiology.
Until now, comparison of software was limited
by arbitrary naming
used in each system.
Standardized terminology and increased functionality of image processing should be offered to the dental profession.
This systematic review summarizes and compares the results of some of the most used methods for dental age
estimation in adults, performing a qualitative and quantitative analysis.

—————————————. Age estimation in adults is a challenge in all forensic
contexts, especially in cases that require the use of non-invasive methods.

In the light of the evidence one could ———————.



1   E. H, said,
G. Fahmy, D. nassar, H. Amar,
“Dental X-ray image segmentation” Biometric Technology for Human
Identification, Proceedings of the SPIE, Vol. 5404, pp. 409417,

E. H. Said, D. E. M. Nassar, G. Fahmy,
H. H. Ammar. “Teeth segmentation in digitized
dental X-ray films using mathematical morphology,” IEEE Transactions on information
forensic and security, vol. 1, Issue. 2, pp. 178189, June. 2006.

Hofer, M. and Marana, A.N., 2007, October. Dental biometrics:
human identification based on dental work information. In Computer
Graphics and Image Processing, 2007. SIBGRAPI 2007. XX Brazilian Symposium on (pp.
281-286). IEEE.

4   Maja Omanovic, Jeff J. Orchard “Exhaustive
Matching of Dental X-rays for Human Forensic
Identification “Journal of the Canadian Society of Forensic Science, 2008 16

5   S.Jadhav, R. Shriram “Dental biometrics used in
forensic science” IJERS/Vol.III/ Issue I/January-March, 2012/26-29

6   Omaima Nomir, M.A.Mottaleb “A system for human
identification from X-ray dental radiographs” Pattern Recognition 38 (2005)
1295 – 1305.

7   S. Dighe, R. Shriram, “Pre-processing,
Segmentation and Matching of Dental Radiographs used in Dental Biometrics”,
International Journal of Science and Applied Information Technology,Volume 1, No.2,
pp 52- 56, May – June 2012

8   S. Kiattisin, A. Leelasantitham, K.
Chamnongthai , K. Higuchi, ” A Match of X-ray Teeth Films Using Image processing
Based on Special Features of Teeth” , SICE Annual Conference 2008, pp 35-39

9   ?. Oprea, C. Marinescu , I. Li??, M. Jurianu,
D. A. Vi?an, I. B. Cioc, ” Image Processing Techniques used for Dental Xray Image Analysis” , Electronics Technology, ISSE
2008, pp 125-129

10  S.A.Ahmed, M.N.Taib, N.E.A.Khalid, R.Ahmad,
H.Taib “Performance of compound enhancement algorithms on dental radiograph
images” WASET-2011 13

Said, E.H., Nassar, D.E.M., Fahmy, G. and Ammar, H.H., 2006.
Teeth segmentation in digitized dental X-ray films using mathematical
morphology. IEEE transactions on information forensics and security, 1(2),

12  Stefan Michel, Saskia M.Koller, Markus Ruh,
Adrian Schwaninger, “Do “Image Enhancement” Functions Really Enhance X-ray Image Interpretation?” Cognitive Science
Journal Archieve 2007

13  Shafer’s Tb. “Textbook of Oral Pathology” sixth
edition, 2006.

14  Sharifah Lailee, Nursuriati Jamil “Segmentation
of Natural Images Using an Improved Thresholding-based Technique” IRIS 2012, Elsevier Procedia engg. Conference pp938-944.

15  T. N. Cornsweet, Visual Perception, Academic
Press, New York, 1970.

16  TM Lehmann, E.Troeltsch, K Spitzer” Image processing and enhancement provided
by commercial dental software programs: A Technical Report”
Dentomaxillofacial Radiology (2002) 31, 264-272.

17  White & Pharoah “Oral Radiology- Principles
and Interpretation”, Fifth Edition (2005), selected illustration by Dr. Donald
O’Connor. ISBN 0-323-02001,published by MOSBY ( An affiliate of Elsevier)