Different Image Segmentation Techniques for Dental Image Extraction

R. BalaSubramanyam et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 7( Version 4), July 2014, pp.173-177
www.ijera.com 173|P a g e
Different Image Segmentation Techniques for Dental Image Extraction R. Bala Subramanyam1, K. Purushotham Prasad2,Dr. B. Anuradha3 1 M. Tech student, Department of ECE, SVU college of Engineering, Tirupati, AP, India 2 Research Scholar, Department of ECE, SVU College of Engineering, Tirupati, AP, India 3 Professor, Department of ECE, SVU college of Engineering, Tirupati, AP, India ABSTRACT
Image segmentation is the process of partitioning a digital image into multiple segments and often used to locate objects and boundaries (lines, curves etc.). In this paper, we have proposed image segmentation techniques: Region based, Texture based, Edge based. These techniques have been implemented on dental radiographs and gained good results compare to conventional technique known as Thresholding based technique. The quantitative results show the superiority of the image segmentation technique over three proposed techniques and conventional technique. Key Words:Thresholding based technique, region based, texture based, edge based techniques.
I. INTRODUCTION
Digital Images are used as one of the most important medium for carrying information in the field of computer vision. By image segmentation, we can extract information or objects of images. This information can be used for other functions for example: human identification, detection of cancerous cells, synthetic aperture radar (SAR) images. So the image segmentation is the first step in the image analysis. Image segmentation is a fundamental step in many areas of computer vision including stereo vision and object recognition. It provides additional information about the contents of an image by identifying edges and regions of similar colour and texture, while simplifying the image from thousands of pixels to less than a few hundred segments. Additionally, image segmentation has applications separate from computer vision; it is frequently used to aid in isolating or removing specific portions of an image. Image segmentation methods are categorized on the basis of two properties discontinuity and similarity. Methods based on discontinuities are called as boundary based methods and methods based on similarity are called Region based methods. Segmentation is a process that divides an image into its regions or objects that have similar features or characteristics. Mathematically complete segmentation of an image R is a finite set of regions R1…Rs. R = Ụi =1 RiRi ∩ Rji≠j
The following sections present the study of basic image segmentation called Threshold based and proposed techniques Region based, Texture based, Edge based. These techniques have been tested on dental radiographs to identify the similarities like infected teeth. In this paper these techniques segment the dental image into regions called dental work (DW). The result section provides simulation results of segmentation techniques. Based on source data (area of DW, distance between DW, angle between DW) of image of different techniques, this paper shows the superiority of technique over proposed and existing techniques.
II. IMAGE SEGMENTATION TECHNIQUES
A. Existed technique: Thresholding Technique:
It is simple image segmentation technique but powerful approach for segmenting images. From a gray scale image, thresholding can be used to create binary images. This technique is based on space regions i.e on characteristics of image. In thresholding process, first convert gray scale image into binary image, by choosing proper threshold value T, divide image pixels into several space regions and separate objects from background. For example f(x,y) is intensity value of image pixel of object, if it is greater than or equal to T i.e., f(x,y)≥T then it belongs to that object otherwise it belong to background. There are two types of thresholding methods with regarding selection of threshold value T: global and local thresholdings. In global thresholding threshold value T is constant where as in local thresholding valueT is variable because of uneven illumination. Threshold selection is typically done interactively however, it is possible to derive automatic threshold selection algorithms. Threshold technique can be expressed as: T=T(x,y),p(x,y),f(x,y)…………………(1)
RESEARCH ARTICLE OPEN ACCESS

Where T is threshold valuex,y are the coordinate of
the threshold value point. p(x,y),f(x,y) are points of
gray scale image.
The threshold image g(x,y) is defined as:
g(x,y) =1 if f(x,y)≥T= 0 if f(x,y)≤T …..……(2)
B. Proposed Techniques:
Texture based segmentation:
Texture segmentation is important task in image
processing. It works at segmenting a textured image
into several regions having similar patterns. Texture
segmentation has been effective and efficient
technique, so used in many applications like in
analysis of biomedical images, seismic images. The
texture feature extraction methods can be classified
into statistical, structural and spectral. . In statistical
approaches, texture statistics such as the moments of
the gray-level histogram, or statistics based on gray-level
co-occurrence matrix are computed to
discriminate different textures. In structural
approaches, “texture primitive”, the basic element of
texture, is used to form more complex texture
patterns by applying grammar rules, which specify
how to generate texture patterns. Finally, in spectral
approaches, the textured image is transformed into
frequency domain.
Region based segmentation:
Region based algorithms are relatively simple
and more immune to noise. Region-based
segmentation is based on the connectivity of similar
pixels in a region. There are two main approaches to
region-based segmentation: region growing and
region splitting. Let R represent the entire image
region. Segmentation is a process that partitions R
into sub regions, R1,R2,…,Rn, such that
Where P(Rk): a logical predicate defined over
the points in set Rk. For example: P(Rk)=TRUE if all
pixels in Rk have the same gray level. Region
splitting is the opposite of region growing. Region
splitting and merging method can divide an image
into a set of arbitrary unconnected regions and then
merge the regions in an attempt to satisfy the
conditions of reasonable image segmentation. Region
splitting and merging is usually implemented with
theory based on quad tree data.
In region splitting and merging method the
procedure is as follows:
Let R represent the entire image region and select a
predicate Q
i) We start with entire image if Q(R) = FALSE we
divide the image into quadrants, if Q is false for
any quadrant that is, if Q (Ri) = FALSE, We
subdivide the quadrants into sub quadrants and
so on till no further splitting is possible.
ii) If only splitting is used, the final partition may
contain adjacent regions with identical
properties. This drawback can be remedied by
allowing merging as well as splitting i.e. merge
any adjacent regions Rj&Rk for which, Q (Rj U
Rk) = TRUE.
iii) Stop when no further merging is possible.
The portioned and corresponding quad tree is
shown in figure 1.
(a) (b)
Figure 1. (a) partitioned image (b)corresponding quad
tree image.
Edge based segmentation:
An object can be easily detected in an image if
the object has sufficient contrast from the
background. Edge-based segmentation represents a
large group of methods based on information about
edges in the image. There are three basic types of
gray-level discontinuities in a digital image: points,
lines, and edges. The most common way to look for
discontinuities is to run a mask through the image.
We say that a point, line, and edge has been detected
at the location on which the mask is centered if |R|≥T
Where R=w1z1+w2z2........+w9z9
Canny edge detection method is a more robust
gradient-based edge detection algorithm. It uses
linear filtering with a Gaussian kernel to smooth the
noise in the image, then it computes the strength and
direction of the edge for every pixel in the smoothed
image by differentiating the image in the horizontal
and vertical directions. Next, it computes the gradient
magnitude as the root sum of squares of the
derivatives and the gradient direction using
arctangent of the ratio of the derivatives. Finally, the
edge strength of each edge pixel is set to zero if its
edge strength is not larger than the edge strength of
the two adjacent pixels in the gradient direction. The
remaining pixels after this process are labelled as
R R i
n
i
 1
(a)
R i n i (b) is a connected region,  1,2,...,
R R i j i j i j (c)   for all and , 
P R i n i (d) ( )  TRUE for  1,2,...,
i j
i j
R R
P R R
r egions and
(e) (  )  FALSE for any adjacent

candidate edge pixels and an adaptive thresholding method is applied on the thinned edge magnitude image to obtain the final edge map. The canny edge detection follows below algorithm:
i. Compute fx and fy.
푓푥= 휕 휕푥 푓∗퐺 =푓∗ 휕 휕푥 퐺=푓∗퐺푥 푓푦= 휕 휕푦 푓∗퐺 =푓∗ 휕 휕푦 퐺=푓∗퐺푦 퐺 푥,푦 is the Gussian function 퐺푥 푥,푦 푖푠푡푕푒푑푒푟푖푣푎푡푒표푓퐺 푥,푦 푤푖푡푕푟푒푠푝푒푐푡푡표 푥:퐺푥 푥,푦 = −푥 휎2퐺(푥,푦) 퐺푦 푥,푦 푖푠푡푕푒푑푒푟푖푣푎푡푒표푓퐺 푥,푦 푤푖푡푕푟푒푠푝푒푐푡푡표 푦:퐺푦 푥,푦 = −푦 휎2퐺(푥,푦)
ii. Compute the gradient magnitude
푚푎푔푛 푖,푗 = 푓푥 2+푓푦 2
iii. Apply non-maxima suppression.
iv. Apply hysteresis edge linking.
III. SIMULATION RESULTS AND DISCUSSIONS
In this paper the image segmented techniques have been implemented in MATLAB 2009a. The test images are shown in figure 2a and figure 3a represents dental radiographs and corresponding segmented images are shown in figures (2b-2e) and (3b-3e). The source data of different techniques are tabulated for test images are tabulated in Table 1 and Table 2 for comparison purpose.
IV. CONCLUSIONS
Image segmented methods are very useful in image processing applications. In this paper, we proposed a comparative study of three image segmented techniques: region based, texture based, edge based. Three conclusions we have made on characteristics of dental works (DW).
i. Based on area dental work: In region based method area for minute detail is considered. So that the area in region based is larger as the total area is considered. The results obtained by texture method similar when compared to other methods but the one advantage is that it can be implemented to color images. Compared to three methods edge based segmentation gives better results.
ii. Based on distance between DWs: In region based method the area, which is considered may be varied accordingly the distance between DW also varies. This gives the advantage of detection of any minute abnormalities in dental images. In both texture and edge methods the distance between DW same as existing methods.
iii. Based on angle between DWs: In all the methods the angle is same as existing method.
Comparing three proposed methods, Region: Gives the freedom for selecting the area of minute detail for dental process. Texture: This method can be implemented for color images and helps when bleeding occurs in dental process. Edge: This is the best method when compared with the above methods as area and angle between DW is accurate.
Table 1. The source data of techniques of dental image 1
Source Data
Thresholding (Existing method- Reference)
Region
Texture
Edge
Area of DW
573 869 363
666 1 1052 1 1 463
573 869 363
664 956 460
Distance Between DW
104 117 0 14
97 103 NaNNaN 117 0 7 NaNNaN 20 0 0 NaNNaN 14 0 0 0 NaNNaN 0 0 0 0 NaN
104 118 0 14
103 117 0 13
Angle Between DW
359 360 0 5
360 359 NaNNaN 360 0 345 NaNNaN 359 0 0 NaNNaN 6 0 0 0 NaNNaN 0 0 0 0 NaN
359 360 0 6
359 360 0 6
Note: NaN = Not a Number.

Table 2. The source data of techniques of dental image2
Source Data
Thresholding
(Existing method-
Reference)
Region
Texture
Edge
Area of DW
387 1006 40907 387 1006 40907 387 1006 40907 447 1124 42624
Distance
Between DW
17 232
0 221
17 232
0 221
17 232
0 221
17 232
0 221
Angle Between
DW
305 354
0 357
305 354
0 357
305 354
0 357
305 354
0 357
Note: NaN = Not a Number.
Figure 2a. Test image radiograph 1
Figure2b. Threshold segmented image
Figure2c. Region based segmented image
Figure2d. Texture based segmented image
Figure 2e. Edge based segmented image
Figure 3a. Test image radiograph 2
Figure 3b. Threshold segmented image
Figure 3c.Region based segmented image
Figure 3d. Texture based segmented image
Figure 3e. Edge based segmented image
Figure 2: Original & Segmented dental
images for sample 1
Figure 3: Original & Segmented dental
images for sample 2
Segmented Image
Segmented Image
Segmented Image
Segmented Image
Segmented Image
Segmented Image
Segmented Image
Segmented Image

REFERENCES
[1] Dental Biometrics: Computer Aided Human Identification System Using the Dental Panoramic Radiograph
[2] Anil K. Jain and Hong Chen “Matching of Dental X-ray images for Human Identification”. Pattern Recognition - The Journal of the pattern recognition society, 37:1519 – 1532.
[3] M.Mahoor and M.Abdel,”Classification and Numbering of Teeth in Dental Bitewing Images", Pattern Recognition, vol.38,pp.577-586.
[4] O. Nomir and M. Abdel- Mottaleb, “Human Identification from Dental X-Ray Images Based on the Shape and Appearance of the Teeth”, IEEE Transactions on Information Forensics and Security, vol. 2, Issue 2, pp. 188 – 197.
[5] W. Burgern and M. J. Burger. Principles of Digital Image Processing Funda-mental Techniques, Springer, (2009).
[6] N. Senthilkumaran and R. Rajesh, "Edge detection techniques for image segmentation - a survey of soft computing approaches", International Journal ofRecent Trends in Engineering, vol.1, pp. 250-255, (2009).
BIOGRAPHIES
R.BALA SUBRAMANYAM studied B. Tech in K.O.R.MCollege of Engineeringaffiliated to JNTUAnathapur and presently pursuingM. Tech in S.V.UCollege of Engineering.
K. PURUSHOTHAM PRASAD, Research Scholar in the Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering
Dr. B. ANURADHA is presently working as Professor inDepartment of ECE, Sri Venkateswara University College of Engineering. She has guided many B.Tech and M.Tech projects. At present Nine Scholars are working for PhD. She has published a good number of papers in journals and conferences

Different Image Segmentation Techniques for Dental Image Extraction

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