2012 International Conference on System Engineering and Technology

September 11-12, 2012, Bandung, Indonesia

978-1-4673-2376-5/12/$31.00 ©2012 IEEE

Image Contrast Enhancement for Film-Based Dental Panoramic Radiography

Suprijanto(1), Gianto(1), E. Juliastuti(1), Azhari(2), Lusi Epsilawati(2) (1) Instrumentation and Control Research Group

Faculty of Engineering Technology-Institut Teknologi Bandung-Indonesia (2) Radiology Department, Faculty of Dentistry,

Padjajaran University Bandung Indonesia

Abstract— The dental panoramic radiography is one of dental imaging that used to visualize the entirety of the maxilla and mandible jaws on the one image planes. Although the direct digital panoramic radiography has been available, however film-based panoramic radiography is still used on the mostly dental clinic and laboratory in Indonesia. The quality of film-based image has significant limitation due to chemical processing and image enhancement cannot be done if required. Therefore, digitized film-based image to digital image was required to allow image enhancements in order to improve the interpretability quality of information in the image. Digitized film-based image is performed using a flatbed scanner on transmission and reflection mode. In this paper, the contrast quality of digital image that scanned using both mode is evaluated based on statistic image characteristic. The results showed that the quality of digitized image using transmission mode is better than using reflection mode. However, if direct digital imaging is used as a gold standard, image enhancement on digitized image is still required. Four methods, i.e. contrast stretching, HE, AHE, and CLAHE are used to attempt improve the quality digitized image. Evaluation of the preference image quality is performed based on objective criterion. The preference image quality for digitized panoramic image can be obtained by using image enhancement based on CLAHE-Rayleigh method, that indicated by the lowest value of mean, standard deviation, RMSE, and average difference and the higher value of NAE and SAE.

Keywords-component; dental panoramic, radiograph, digitized image, image enhancement

I. INTRODUCTION

X-ray imaging has been available to dental practitioners in the past few decades and currently this imaging is widely used. Two dental radiography techniques are often used, i.e. intra-oral and extra-oral. On intraoral technique, the radiographic film or sensor is placed inside the mouth and placing the radiographic film or sensor outside the mouth, on the opposite side of the head from the X-ray source, produces an extra-oral radiographic view[1].

Panoramic radiography are extra oral technique, in which the film is exposed while outside the patient's mouth. The panoramic image can be visualized the entirety of the maxilla and mandible jaws on the one image planes. Currently, film-based panoramic radiography is widely used on the mostly dental clinic and laboratory in Indonesia, although the direct digital panoramic radiography has been available. For visual

analysis, dental radiologist placing negative film in front of the viewer. The quality of film-based image has significant limitation due to chemical processing and the image enhancement of the film-based image cannot be done if required, since the level of contrast image is only depend on the level of light intensity of viewer. One of solution to allow improve the quality of contrast image is digitized film-based image to digital image. Digitized film-based image is performed using flatbed scanner on transmission and reflection mode. Scanners are preferred to digital cameras as they practically eliminate optical distortion and the reflection from the surface of the radiograph that would otherwise reduce image quality. However, often digitized film-based image has results undesired brightness and contrast of image. Therefore, image enhancement was required in order to improve the quality of image. [2,3,4]

In this paper, the contrast quality of digital image that

scanned using transmission and reflection mode is evaluated based on mean and standard deviation of the image. Furthermore, the quality of digital image is enhancement based on spatial technique using contrast stretching, histogram equalization (HE), adaptive histogram equalization (AHE), and contrast limited adaptive histogram equalization (CLAHE). Evaluation of the preference image quality is performed based on an objective criterion.

II. DIGITIZED FILM BASED OF DENTAL PANOROMIC Digitized film-based of dental panoramic is performed by using flatbed scanner. Dual mode flatbed scanner, i.e. scanning an object on transmission and reflection mode is used.

Figure 1. Dual Mode Flatbed Scanner

The illustration dual mode flatbed scanner shown on Figure 1. The parameters that used on digitized film is spatial resolution 800 dpi with 8 bit gray level.

III. IMAGE CONTRAST ENHANCEMENT Image enhancement on digital panoramic radiography is

basically improving the interpretability quality of information in image for dentistry. The basic principle of image enhancement is to manipulate attributes of an image to make more suitable for a specific observer. During this process, one or more attributes of an image are modified. The choice of attributes and the way they are modified are specific to a given task. Moreover, the observer’s experience will introduce a great deal of subjectivity into choice of image enhancement methods. The alternative approach to evaluate the preference image quality can be performed using on an objective criterion

On this paper, the quality of digital image is enhancement based on spatial technique using contrast stretching, histogram equalization (HE), adaptive histogram equalization (AHE), and contrast limited adaptive histogram equalization (CLAHE).

A. Contrast Stretching Contrast stretching is a point image enhancement method, that attempts to improve the contrast in an output image by 'stretching' the range of intensity values it contains to span a desired range of values. If an image is attempted have intensity with range 0-2B-1, range of contrast image can be transformed by:

[ ]

[ ]

( ) [ ] [ ]

( ) [ ]

0 ; , min, min

, 2 1 ;min , maxmaks min

2 1 ; , max

B

B

a m na m n

b m n a m n

a m n

⎧ ≤⎪

−⎪= − ⋅ < <⎨ −⎪⎪ − ≥⎩

(1) With b is intensity value of enhanced image on coordinate [m,n], a is original image, B is bit number, min and max are range contrast original image on percentile. [5]

B. Histogram Equalization Histogram based techniques for image enhancement, are mostly based on equalizing the histogram of the whole image which increase the dynamic range corresponding to the image. In other words, the pixel value of enhanced image depends on global pixel value of the origin image. The process of histogram equalization is attempts to change the histogram through the use of a function b = ƒ(a) into a histogram that is constant for all brightness values, with equation:

( ) ( ) ( )2 1Bf a P a= − ⋅ (2) Where P(a) is the probability distribution function. In other words, the quantized probability distribution function normalized from 0 to 2B–1 is the look-up table required for histogram equalization. [5]

C. Adaptive Histogram Equalization (AHE) Adaptive histogram equalization (AHE) differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct region of the image, and uses them to redistribute the lightness values of the image. The objective of process is to improve the local contrast of an image. The process steps on AHE are consists of define local neighborhood of image pixel in specific section, calculation and equalization histogram neighborhood and mapping of pixel value on the center of neighborhood based on equalized local histogram. To avoid discontinue value between the specific region, bilinear interpolation is applied to estimate pixel value on the region. It is therefore suitable for improving the local contrast of an image and bringing out more detail. [5,6,7,8]

D. Contrast Limited Adaptive Histogram Equalization Contrast limited adaptive histogram equalization (CLAHE) is an image enhancement method that develop from AHE method. CLAHE method utilize a parameter limit value of histogram in order to obtain adequate brightness and contrast on the enhanced image. The contras limit is controlled on the histogram equalization process. Criterion of desired histogram distribution is defined based on contrast range of original image. Some criterion of form of histogram distribution is uniform, exponential, or Rayleigh. Therefore, the natural property of original image can be maintained since the distribution form is not significant change after enhancement process. [5,6,7,8]

IV. OBJECTIVE CRITERION OF CONTRAST IMAGE QUALITY The performance of image enhancement methods could be evaluated using objective criterions. It criterion was derived based on statically characteristic of images, such as Mean, Standard Deviation, Peak Signal to Noise Ratio (PSNR), and Root Mean Square Error (RMSE), Average Difference (AD), Normalized Absolute Error (NAE), Structural Content (SC). [6,12,13] Mean is an average of gray level image pixel, that defined as:

1

1 N

ii

x xN =

= ∑ (3)

where x is average value, N pixel numbers, and xi is gray level value on i-pixel. Its often can used to measure brightness level of image. For image with gray level intensity 0-255, the image with ideal visual representation has mean(μ) in between 100-200[9]. Standard deviation is measure statistic distribution of gray level image pixel, that defined as:

( )1

1 N

ii

x xN

σ=

= −∑ (4)

where σ is standard deviation, x is average value, N pixel numbers, and xi is gray level value on i-pixel. Its often can used to measure contrast of image. For image with gray level intensity 0-255, the image with ideal visual representation has standard deviation(σ) in between 40 – 80[9]. Peak Signal to Noise Ratio (PSNR) represent quality of image that comparison between noise relative to signal peak. PSNR value is defined as:

25520 log10PSNRMSE

⎛ ⎞= ⎜ ⎟⎝ ⎠

(5)

With MSE is value of mean square error that defined as:

( ) ( )211

0 0

1 , ,yx nn

x y

MSE r x y t x yn n

−−

= −⎡ ⎤⎣ ⎦∑∑

(6)

where nx and ny is number of row and column-image, r(x,y) and t(x,y) is a gray level value of pixel from originally and enhanced image, successively. Root Mean Square Error (RMSE) is square root of MSE that used to measure average error of enhanced image relative to original image. RMSE is defined as:

( ) ( )211

0 0

1 , ,yx nn

x y

RMSE r x y t x yn n

−−

= −⎡ ⎤⎣ ⎦∑ ∑ (7)

Average Difference (AD) is measure an average difference among pixel value, that defined as

( ) ( )11

0 0

1 , ,yx nn

x y

AD r x y t x yn n

−−

= −∑∑ (8)

Normalized Absolute Error (NAE) is average of absolute difference between the originally and enhanced image and divided by originally image, that formulated as as

( ) ( )

( )

11

0 011

0 0

, ,

,

yx

yx

nn

nn

r x y t x yNAE

r x y

−−

−−

−=∑∑

∑∑ (9)

Structural Content (SC) is also correlation based measure the similarity between the originally and enhanced image , that formulated as

( )

( )

112

0 0211

0 0

,

,

yx

yx

nn

nn

r x ySC

t x y

−−

−−=∑∑

∑∑ (10)

V. RESULTS AND ANALYSIS

A. Digitized Images The negative film of dental panoramic is obtained from radiography laboratory, Radiology Department, Faculty of Dentistry, Padjajaran University. Figure 2, shown comparison between digitized image that obtained using scanner and direct digital imaging. The value of statistic characteristic of digitized image using scanner on dual modes are compared with image that taken from direct digital imaging (see Table 1). The result shown that direct digital imaging has brightness level ( x ) and contrast (σ) on the ideal range (100< x <200) and 40<σ<80). [9,10]

(a)

(b)

(c)Figure 2. Digitized image and histogram that taken from (a) reflection mode (b) transmission mode and (c) direct digital imaging

Table 1. Evaluation of digitized image

Digital image x σ Pixel numbers < 64

Pixel numbers > 192

Reflection 41.53 27.03 86.94 % 0.08 % Transmission 84.65 46.75 36.58 % 3.66 % Direct Digital 125.89 55.27 15.38 % 12.48 %

Comparison with digitized image that converted using scanner, digitized image using transmission mode has brightness level and contrast better than reflection mode. However, digitized image that converted using scanner, degradation of brightness level is occur. Furthermore, a contrast range is distribute on a gray level tend to lower values. Therefore, digitized image using transmission mode is

Original Grayscale Image Sample AT8-800-001

0

0.5

1

1.5

2

2.5

3

3.5

4

x 105 Histogram of original image Sample AT8-800-001

0 50 100 150 200 250

Original Grayscale Image Sample Adhy Ganjar

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 10

4 Histogram of original image Sample Adhy Ganjar

0 50 100 150 200 250

still required to enhance, in order has image quality close to the result form direct digital imaging.

B. Results of Image Enhancement The results of image enhancement with several methods and also plot of histogram distribution was showed on Figure 3. Enhanced image using contrast stretching is increasing brightness level of visual perception. With objectively, the brightness level has mean pixel value changing from 84.65 to 138.82. Drawback of contrast stretching is changing local contrast that caused reduced detail an object on the image. Using histogram equalization, contrast is increasing on the range dominant gray level (i.e. in region of peak histogram). Drawback of histogram equalization is changing local contrast that caused reduced detail an object on the image, because on a specific region is tend to under exposed (dark) or over exposed (bright).

(a)

(b)

(c)

(d)

(e)

(f)

Figure 3. Enhanced image and histogram that obtained using (a) contrast stretching, (b) HE, (c) AHE, (d) CLAHE-Uniform, (e)

CLAHE-Exponential, (f) CLAHE-Rayleigh Using AHE method, the drawback results of histogram equalization can be solved, particularly problem on reduced detail an object on the image due to change local contrast. The visual perception of image brightness is tend to natural than HE results. However, the drawback of AHE method is increasing noise contrast on the background in all image region. As extended AHE method, on CLAHE method is give opportunity to make some boundary value of contrast and brightness on image during process of image enhancement. Problem increasing contrast on background noise can be solved on CLAHE method. Moreover, CLAHE method can “forced” histogram distribution to transform into Rayleigh and uniform distribution. Figure 4 is give illustration the effect of Rayleigh and uniform distribution on the image results. In general, CLAHE method can enhance brightness level into specific range and also keep natural that occur on the original images.

(a)

(b)

Figure 4. Comparison method (a) CLAHE-uniform (b) CLAHE-rayleigh in order to hold local contrast and detail object on dental

panoramic images

With objective criterion, assessment of image enhancement panoramic image is shown on Figure 5.

Image Sample AT8-800-001 by CS

0

1

2

3

4

5

6

7

8

x 105 Histogram of image Sample AT8-800-001 by CS

0 50 100 150 200 250

Image Sample AT8-800-001 by HE

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

5 Histogram of image Sample AT8-800-001 by HE

0 50 100 150 200 250

Image Sample AT8-800-001 by AHE

0

0.5

1

1.5

2

2.5

3

3.5

x 105 Histogram of image Sample AT8-800-001 by AHE

0 50 100 150 200 250

Image Sample AT8-800-001 by CLAHE-Uniform

0

0.5

1

1.5

2

2.5

3

3.5x 10

5 Histogram of image Sample AT8-800-001 by CLAHE-Uniform

0 50 100 150 200 250

Image Sample AT8-800-001 by CLAHE-Exponential

0

0.5

1

1.5

2

2.5

3

3.5

x 105Histogram of image Sample AT8-800-001 by CLAHE-Exponential

0 50 100 150 200 250

Image Sample AT8-800-001 by CLAHE-Rayleigh

0

0.5

1

1.5

2

2.5

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3.5

4

x 105 Histogram of image Sample AT8-800-001 by CLAHE-Rayleigh

0 50 100 150 200 250

Figure 5. Plot of objective criterion of contrast image quality

Based on Figure 5, correlation between image characteristic related with performance of image enhancement method is demonstrated. For parameters mean, standard deviation RMSE, and average difference, the lowest value is related to increasing quality of image enhancement method, and for parameters NAE and SC, the higher value is related to increasing quality of image enhancement method. From the assessments, it is shown that image enhancement using CLAHE-Rayleigh give more optimal quality than the other method.

VI. CONCLUSION In this paper, the contrast quality of digital image that scanned using transmission and reflection mode is evaluated based on statistic image characteristic. Furthermore, the quality of digital image is enhancement based on spatial technique using contrast stretching, HE, AHE, and CLAHE. Evaluation of the preference image quality is performed based on objective criterion.

The results showed that the level brightness and contrast of digitized image using transmission mode is higher than using reflection mode. However, digitized image that converted using scanner, degradation of brightness level is occur and a contrast range is distribute on a gray level tend to lower values. From the objective assessments, it is showed that image enhancement using CLAHE-Rayleigh give more optimal quality than the other method that evaluated in the paper. If the direct digital image as gold standard has brightness level ( ) and contrast (σ) on the ideal range (100<

<200) and 40< σ<80), the resulting CLAHE-Rayleigh has

also has brightness level ( ) and contrast (σ) on the ideal range. Therefore, the preference image quality for digitized panoramic image can be obtained using CLAHE method.

ACKNOWLEDGMENT The authors acknowledge with gratitude the great support

from decentralization research program 2012, Higher Education Agency, Ministry of Education and Culture, Republic Indonesia.

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