Yonago Acta medica 2013;56:85–91

Original Article

Evaluation of a Noise Reduction Procedure for Chest Radiography
Ryohei Fukui,* Rie Ishii,* Kazuhiko Kodani,* Yoshiko Kanasaki,† Hisashi Suyama,‡ Masanari Watanabe,§ Masaki
Nakamoto§ and Yasushi Fukuoka§*Division of Clinical Radiology, Tottori University Hospital, Yonago 683-8504, Japan, †Division of Radiology, Matsue City Hospital,
Matsue 690-8509, Japan, ‡Division of Internal Medicine, Tottori Central Prefectural Hospital, Tottori 680-0901, Japan and §Divisionof Medical Oncology and Molecular Respirology, Tottori University Hospital, Yonago 683-8504, Japan

ABSTRACT

caused by the characteristics of the X-ray detector andvariations in radiation doses. Reducing the radiation dosefor patients is important, and the graininess in digital imaging is most affected by dose reduction. Therefore, theimprovement of graininess can actually lead to reducing
radiation doses.1 There are many reports on image processing of the X-ray imaging,2, 3, 4, 5, 6 but excessive image processing can deteriorate image quality.7, 8
The noise reduction procedure (NRP) is a function of the image processing software MLT-S (Canon
Lifecare Solutions, Osaka, Japan). NRP analyzes theimage noise superimposed on each spatial frequency
component, and reduces noise by subtracting the noisefrom each spatial frequency component. Our aim in
this study was to verify the usefulness of NRP in chest
radiography compared with the current image processing. We performed 2 types of evaluations in the present
study, a physical characteristic analysis of graininess,and observation. However, the 2 types of evaluations
may not be correlated. Therefore, to optimize the image
processing procedure conditions for chest radiography,
we performed an overall evaluation of chest radiography using a phantom and various clinical cases duringobservation.

Background The aim of this study was to evaluate theusefulness of noise reduction procedure (NRP), a function in the new image processing for chest radiography.
Methods A CXDI-50G Portable Digital Radiography
System (Canon) was used for X-ray detection. Imagenoise was analyzed with a noise power spectrum (NPS)
and a burger phantom was used for evaluation of density resolution. The usefulness of NRP was evaluatedby chest phantom images and clinical chest radiography. We employed the Bureau of Radiological Health
Method for scoring chest images while carrying out ourobservations.
Results NPS through the use of NRP was improved
compared with conventional image processing (CIP).
The results in image quality showed high-density resolution through the use of NRP, so that chest radiography
examination can be performed with a low dose of radiation. Scores were significantly higher than for CIP.
Conclusion In this study, use of NRP led to a highevaluation in these so we are able to confirm the usefulness of NRP for clinical chest radiography.
Key words chest radiography; image processing;noise power spectrum; noise reduction procedure; observation

SUBJECTS AND METHODS
X-ray devices and phantoms

A DHF-1510S (Hitachi Medical, Tokyo, Japan) was used
for X-ray equipment, and a CXDI-50G (Canon) wasused as the X-ray detector. This detector is a terbium
doped gadolinium oxysulfide (Gd2O2S:Tb) FPD employing indirect conversion with a pixel pitch of 0.15 mm. A
burger phantom was used to evaluate density resolution,
and a PBU-SS-2 chest phantom (Kyoto Kagaku, Kyoto,
Japan), which simulates the chest structure, to simulate

For digital radiography, the conventional film-screen
system for X-ray radiography is replaced with computed
radiography and a flat panel detector (FPD). In analog radiography, film density and changes in contrast
depend on the exposure conditions, whereas in digitalradiography, the X-ray image is dependent on the image
processing that incorporates various parameters. The
image processing procedure changes the exposure conditions for the X-ray device. Suitable radiograms can beachieved with digital radiography even by an inexperienced radiological technologist.
Digital X-ray image quality is adjusted based on 3
factors: contrast, sharpness and graininess. Optimizationof image quality is affected by these 3 closely related factors. The graininess on an X-ray image is statistical noise

Corresponding author: Ryohei Fukui
rfukui@med.tottori-u.ac.jp
Received 2013 September 24
Accepted 2013 November 5Abbreviations: BMI, body mass index; BRH, Bureau of Radiological Health; C-D, contrast-detail; CIP, conventional image processing; FPD, flat panel detector; IQF, image quality figure; NIP,
new image processing; NPS, noise power spectrum; NRP, noise
reduction procedure85

R. Fukui et al.

chest radiography. Both phantoms were used for observation. A high-resolution monitor ME355i2 (TOTOKU,Tokyo, Japan) was also used for observation. Images
were analyzed using ImageJ image processing (NIH,
Bethesda, MD).
Image processing for evaluation

An X-ray image processed by the current method isreferred to as “conventional image processing (CIP)” in
the present study. CIP is used in our hospital for chest
radiography. An X-ray image processed by MLT-S isreferred to as “new image processing (NIP)”. The noise
reduction factor in NRP can be changed from 1 to 10
during radiography image processing. NRP with the associated noise reduction factor is referred to as NIP (factor
1) to NIP (factor 10), in the order of increasing image
processing strength.
NRP is able to reduce image noise that is strengthened during image processing. Although image processing strengthens the noise, the radical nature of an image
will reduce because of the method of noise reduction
with NRP. Detection of a small signal is reportedly
reduced by excessive processing.7 Therefore, in thisstudy, we employed NIP (factor 5) and NIP (factor 7)
for moderately and slightly strong image processing,respectively. Other processing conditions, for example,
contrast and edge enhancement, were selected based on
manufacturers’ recommendations.

Fig. 1. Burger phantom X-ray image. The burger phantom is aplate used to evaluate density resolution; the depth becomes gradually shallow laterally, and the diameter changes in the lengthwise
direction.

a lengthwise direction. The same geometric parameters
were used with the burger phantom image for the clinical chest radiography. We placed an acrylic plate (20cm) in front of the burger phantom to simulate the average thickness of a human body. Figure 1 shows the Xray image of the burger phantom. The observers evaluated the limit of detection with this signal. CIP and NIP(factor 5) were used for this evaluation. Eleven radiological technologists (experience: 2–36 years) in our hospital
participated in the observation. The density resolution
was calculated from the results of the observation andexpressed as a contrast-detail (C-D) diagram. The curveof the C-D diagram (below) shows high-density resolution. The image quality figure (IQF) was calculated fromthe same results.9 The IQF, obtained from n lines of theburger phantom, was calculated using the formula:

Ethical considerations

The Ethics Committee of Tottori University approved
the use of clinical images for the experiment (approval
number 1143). We explained the study to the patients,and only the images provided were used for this study
after obtaining consent from them.

n

IQF =

Ci × Di.min

(1)

i=1

Measuring the NPS

X-ray image noise was evaluated using the noise power
spectrum (NPS). A decrease in the NPS value indicates
an improvement in graininess. Images that emitted Xray uniformly were used for NPS analysis. Only theimage processing on the FPD console was changed foreach acquired image, and these images were used for
subsequent NPS analysis after output in formats used for
digital imaging and medical communication. CIP and
NIP (factor 5) were chosen for NPS analysis.

where Ci is the signal diameter of line i, and Di is thesignal thickness in line i detectable by an observer.
Di decreases as the density resolution increases. Thus,a small IQF denotes a high density resolution. To investigate the change in IQF according to exposure dose,
we prepared the evaluation samples with NIP(factor 5)that were obtained at various exposure times. An exposure time of 25 ms is equivalent to an exposure dose of
100%: we employed exposure times of 20 ms (80%), 16
ms (64%) and 12 ms (48%).

Evaluation of density resolution

Evaluation using a chest phantom

The density resolution was evaluated by observation
through the use of the burger phantom, which is a plateused to evaluate density resolution; the depth becomes
gradually shallow laterally, and the diameter changes in

The chest phantom simulates the chest structure for use
in chest radiography. Radiograms of the chest phantom
were acquired to compare image-processing methods
86Evaluation of a noise reduction procedure
Table 1. Assessment items of the BRH method: (a) anatomic landmarks and (b) physical parameters(a) Anatomic landmarks
i Bony thorax (ribs & clavicles)
Cortical margins
a Optimally visualized(clearness of the edge)		
b Adequately visualized			c Poorly visualized			d Not visualized		Trabeculae
a Optimal detail			b Adequate detail			c Poor detail			d Not visualizedii Retrocardiac area
Left diaphragma Totally visualized			b Partially visualized			c Not visualized		Spine
a Too well visualized			b Optimally visualized			c Acceptably visualized			d Poorly visualized			e Not visualizediii Diaphragm (diaphragm outline)		
a Both visualized			b Right only			c Left only			d Not visualizediv Trachea (visible to)		
a Left main stem bronchus			b Carina			c Neck and upper mediastinum			d Not visible
v Pulmonary vasculature 		a Right costophrenic angle(maximum measurable to)		
b Right mid-lung			c Right descending pulmonary artery			d None(b) Physical parameters
vi Contrast		
a Optimal			b Good		
Poor, but diagnostic
c Too gray			d Too black/white		
Unacceptable,
e Too gray		
not diagnostic
f Too black/whitevii Graininess		
a No grain visible			b Minimal grain			c Grainy, but dose not interfere with diagnosis			d Grain interferes with diagnosis
viii Density		
a Optimal			b Good		
Poor, but diagnostic
c Too dark			d Too light			e Unacceptable
ix Detail		
a Optimal			b Good			c Poor detail, but dose not interfere with diagnosis			d Lack of detail interferes with diagnosis( ) shows the score when the observer selected on the chest radiogram evaluation.
Reference for Table 1：

87

(5)
(4)
(2)
(0)
(5)
(4)
(2)
(0)
(15)
(7)
(0)
(7)
(15)
(7)
(3)
(0)
(15)
(7)
(7)
(0)
(15)
(10)
(5)
(0)
(30)
(20)
(10)
(0)
(35)
(23)
(11)
(11)
(0)
(0)
(20)
(13)
(6)
(0)
(15)
(10)
(5)
(5)
(0)
(30)
(20)
(10)
(0)

R. Fukui et al.

Fig. 2. Noise power spectrum (NPS).
The characteristic for horizontal direction and vertical direction is referred toas “parallel” and “perpendicular”, respectively. The NPS with NIP (factor 5)
was smaller than that CIP in all spatial
frequencies. CIP, conventional image
processing; NIP, new image processing.

for X-ray radiography were as follows: upright position,
posterior-anterior view, focus to detector distance of 200
cm. Four pulmonologists and 2 radiologists took part inthis observation experiment. All observers were preppedusing other samples of radiograms of the chest phantom
beforehand and confirmed the evaluation region and criterion. CIP, NIP (factor 5) and NIP (factor 7) were used
to process the chest radiograms, and the BRH method
was used for scoring the images. All patients were classified and evaluated using the same methods. Classifications are described in the following section.

CIP, NIP (factor 5) and NIP (factor 7). The geometry forthe chest phantom radiography is similar to that for chest
radiography of patients in our hospital. The observers forthis experiment were the same observers in the burger
phantom study. Observation was similar to that done
for the clinical interpretation of radiograms (i.e. room
lighting, high-resolution monitor, etc.). We employed the
Bureau of Radiological Health (BRH) method for scoring the chest phantom images (Tables 1a and b).10 The
BRH method is used for the overall evaluation of chest
radiography using a screen or film. The chest radiography score was calculated as an anatomical index (perfectscore: 100 points) and a physical index (perfect score:
100 points). We changed the grand total to an overall
perfect score of 100 using the following formula:
Total score of AI + total score of PI
× 100
200 – N

Subject classifications for chest radiography

All patients were classified according to body mass index (BMI), aeration of the lung and presence of a lunglesion, and evaluated with only 2 processing methods,
CIP and NIP (factor 5).
Because graininess is most affected by variations ina patient’s physique, we classified all patients by BMI. A
BMI less than 18.5 was classified as underweight; a BMI
of 18.5 but less than 25, as normal and a BMI more than25, as overweight.11
Evaluations of chest radiograms are also affectedby density variation in the lung field caused by aeration.
The right diaphragm should be at the height of the posterior 10th rib under sufficient inhalation.12 When the right
diaphragm was positioned at the height of the 9th rib,the case was classified as “poor”. The case was classified
as “good” when the right diaphragm was positioned at
any height below the 9th rib.
To optimize image processing, it is important toevaluate chest radiograms with various lesions. We classified all patients with lung lesions into 4 patterns: interstitial markings, a nodule, a consolidation and a normal
pattern.13(2)

where AI is the anatomical index, N, the evaluation item
and PI, the physical index. However, if the observer
could not evaluate an image because of a lesion, we deducted the score for the N from the denominator. The
Wilcoxon signed-rank test was used to test for statisticalsignificance, with the significance level set at P < 0.05.
Evaluation using clinical chest radiography

Forty patients [28 men, 12 women, mean (s) age: 63.9
(16.0)] involved in diagnostic chest radiography in our
hospital (examinations: February to April, 2009) were
subjected to the observation experiment. Diagnoses included 5 normal subjects, 13 patients with lung cancer(including postoperative diagnosis), 13 patients withinterstitial pneumonia, 3 patients with sarcoidosis, 2 patients with nontuberculous mycobacterial infection and4 patients with other diseases. The geometry conditions
88

Evaluation of a noise reduction procedure

RESULTS
Figure 2 shows the NPS analysis results. An
NPS with an NIP was smaller than that with
CIP for all spatial frequencies. Moreover, the
NPS was small when the image processing
was strong.
Figure 3a shows the C-D diagram calculated in the burger phantom study. The curvefor NIP (factor 5) was below that for CIP. Figure 3b shows the IQF value for each exposure
dose. As shown in Fig. 3b, the IQF value with
CIP was equal to the IQF value at 72% exposure dose with NIP (factor 5).
Table 2 shows the results of the observation with the chest phantom image. The total
score for NIP (factor 7) was significantly
higher than that for CIP. There was no difference in the total score between CIP and NIP(factor 5). Many assessment items for the BRH
method with NIP were improved comparedwith CIP; in particular, the graininess score
was significantly higher.
Table 2 also shows the results of observation with clinical chest radiography. The total
Fig. 3. The results of observation using the burger phantom.
scores of NIP (factor 5) and NIP (factor 7)
a: Contrast-detail diagram.
were significantly higher than CIP (P = 0.0002,
b: Image quality figure.
CIP, conventional image processing; NIP, new image processing.
P = 0.007, respectively). The contrast and thegraininess were improved compared with CIP.
Because the evaluation of NIP (factor 5) wasin Table 3. For BMI, the scores of many assessment
higher than that for NIP (factor 7), we estimated only
items for the BRH method were significantly higher
CIP and NIP (factor 5) in the subsequent experiment inthan CIP. There was a tendency for the score to decrease
which all clinical cases were categorized.
as the BMI value increased; however, NIP (factor 5)
Table 3 shows the results of observation with clinicalreduced the magnitude of this decrease compared withcases categorized according to various conditions of the
CIP. For aeration of the lung, both the good group andpatients. For convenience, the CIP score is not included

Table 2. The results of observation with the chest phantom and chest radiography
Assessment items

Chest phantom (mean [s])		
CIP
NIP (factor 5)
NIP (factor 7)

Chest radiography (mean [s])CIP
NIP (factor 5)

NIP (factor 7)

Cortical margins
Trabeculae
Left diaphragm
Spine
Trachea
Diaphragm
Pulmonary vasculature
Contrast
Graininess
Density
DetailTotal score

3.67 [0.96]
2.89 [1.0]14.1 [3.5]8.33 [5.0]10.7 [3.1]
15.0 [0.0]25.6 [5.1]
20.3 [6.7]13.0 [4.9]8.89 [1.9]
21.1 [4.1]72.0 [21]4.09 [0.82]
3.49 [1.3]
11.1 [5.5]6.25 [4.7]
13.1 [3.5]11.7 [5.5]20.1 [11]18.0 [8.9]18.3 [3.0]9.26 [2.9]18.4 [5.4]70.0 [11]4.21 [0.65]*
3.44 [1.3]
11.4 [5.3]
8.04 [5.0]*
13.4 [3.3]12.1 [5.2]
20.8 [11]19.0 [9.2]
19.5 [1.8]*
9.79 [2.6]*
18.6 [5.0]72.8 [11]*4.22 [0.90]
3.78 [1.1]
14.1 [2.2]7.89 [4.6]10.4 [2.7]15.0 [0.0]23.7 [6.0]20.3 [6.7]18.4 [3.1]*
8.89 [2.2]
21.1 [4.1]
73.8 [19]4.22 [0.83]
3.56 [1.2]
14.1 [2.2]8.33 [4.5]10.4 [2.9]15.0 [0.0]25.6 [5.1]
24.3 [4.5]18.4 [2.6]*
8.89 [2.2]
21.1 [3.8]80.4 [15]*CIP, conventional image processing; NIP, new image processing.
*P < 0.05: Comparison with the score of CIP.

89

4.27 [0.65]*
3.51 [1.4]
11.2 [5.4]
8.13 [4.8]*
13.5 [3.4]12.0 [5.2]
20.9 [11]19.6 [9.2]*
19.5 [1.8]*
10.2 [2.6]*
19.3 [5.1]*
74.0 [11]*R. Fukui et al.
Table 3. The results of observation with clinical cases categorized according to various conditions of the patients
BMI (mean [s])		
Aeration of the lung (mean [s])UnderNormal
Over-			Normal
weightrange
weightPoor
Goodpattern

Lung lesion (mean [s])Interstitial		Consolimarkings Nodule
dationCortical margins 4.37 [0.67]* 4.29 [0.65]* 4.10 [0.71]* 4.15 [0.67]* 4.34 [0.61]* 4.40 [0.67] 4.12 [0.72]* 4.38 [0.61]* 4.32 [0.55]*
Trabeculae
3.47 [1.4]* 3.49 [1.39]* 3.67 [1.3] 3.35 [1.5]* 3.61 [1.29]* 3.60 [1.2]* 3.42 [1.5]* 3.48 [1.4] 3.57 [1.3]*
Left diaphragm 12.6 [3.7]* 11.5 [5.3]* 8.63 [6.6] 10.3 [5.8] 11.4 [5.3]* 13.9 [2.8]* 9.10 [6.1] 13.5 [3.2]* 10.6 [5.8]Spine
9.27 [4.7]* 8.26 [5. 0]* 6.23 [4.1] 6.92 [4.7] 8.68 [4.9] 8.87 [4.3] 6.67 [4.9] 7.71 [4.9] 9.24 [4.8]*
Trachea
11.9 [3.8]* 13.8 [3.1]* 13.8 [3.9]* 13.9 [2.6]* 13.4 [3.8]* 13.6 [3.5]* 14.4 [2.5]* 12.6 [3.9]* 13.5 [3.7]*
Diaphragm
14.2 [2.4]* 12.3 [5.0]* 9.33 [6.0]* 11.3 [5.7] 12.1 [5.1]* 14.7 [1.5]* 11.3 [5.4] 14.5 [2.0]* 10.1 [6.3]*
Pulmonary
23.4 [9.5] 20.5 [11]* 19.1 [12]* 19.3 [12] 21.2 [11]* 27.1 [5.0]* 15.0 [12]* 27.4 [5.9]* 19.0 [11]vasculature
Contrast
21.1 [8.8]* 20.0 [9.2]* 16.6 [7.5] 18.2 [8.7] 20.0 [9.6]* 25.8 [8.0] 18.1 [9.7] 20.8 [8.2]* 17.8 [8.8]*
Graininess19.5 [1.8]* 19.5 [1.8]* 19.3 [2.1]* 19.3 [2.1]* 19.5 [1.8]* 19.5 [1.8]* 19.5 [1.8]* 19.1 [2.3]* 19.6 [1.6]*
Density
10.5 [2.4]* 10.2 [2.7]* 9.17 [2.3] 9.68 [2.6] 10.4 [2.6]* 11.7 [2.7] 9.25 [2.7] 10.5 [2.36]*10.2 [2.3]*
Detail19.0 [5.5]* 19.6 [4.8]* 17.7 [5.7] 18.5 [5.1]* 19.5 [4.8]* 21.3 [5.7] 18.0 [5.8]* 19.2 [4.5]* 19.6 [4.3]*
Total score74.7 [11]* 71.7 [11]* 63.8 [12]* 67.4 [12]* 72.1 [11]* 82.3 [9.7]* 64.4 [13]* 76.6 [9.3] 68.8 [8.1]*
BMI, body mass index; CIP, conventional image processing; NIP, new image processing.
*P < 0.05: Comparison with the score of CIP.

The score for NIP (factor 7) was significantly higher
than CIP in the phantom study; however, the score for
NIP (factor 5) was the opposite in the clinical study.
The chest phantom image is unvaried compared withthe clinical image. Thus, observer performance likely
improved because the evaluation became easier, i.e., thedecrease in image noise was enhanced by noise reduction, which facilitated evaluation. Some reports suggestthat excessive image processing deteriorates X-ray image quality.7, 8 In this study, the moderate processing
NIP (factor 5) was more useful than the strong processing NIP (factor 7). This result corresponded with theresults of past reports. We were also able to confirm theusefulness of NIP (factor 5) in clinical cases with interstitial markings or high BMI. In cases of diffuse interstitial markings, the contrast and density of the lung are
reduced, which inhibits recognition of the pulmonary
vasculature and the minor fissure. Using NIP (factor 5),
the discrimination in the lung field was improved bynoise reduction and frequency emphasis, thus the score
improved. There was a tendency for the overall score of
the high BMI group to decrease; however, the score improved by 13% using NIP (factor 5). Figure 4 shows the
distribution of BMI over 1 week in patients examined bychest radiography. Many patients had a BMI above thenormal range. This led us to believe that there are many
imaging situations where NRP can be effective.
In this study, strong image processing decreased theimage noise in the physical characteristic analysis; however, moderate image processing was most suitable forthe evaluation of clinical cases. Consequently, we wereable to optimize the image processing for chest radiography by using NRP. These processes for optimization
will likely benefit any radiography facility.

the poor group scores were significantly higher than CIP.
In the lung lesion category, the total score was higher inorder of a normal pattern, a nodule, a consolidation andinterstitial markings. The total score was significantly
improved in comparison with CIP, except in the nodule
group.
DISCUSSION
By calculating the NPS, we confirmed the effect ofnoise reduction by NRP. The NPS values with NRP
were small for all spatial frequencies, which indicatesthat NRP can reduce image noise in various chest structures. The difference in density resolution between the 2processing methods was confirmed by the C-D diagram.
Figure 3 shows that the 72% of IQF with NIP (factor 5)
was equal to 100% of IQF with CIP. Therefore, it is possible to reduce the radiation dose when using the NRP.

Fig. 4. The distribution of BMI over 1 week in patients examinedby chest radiography. Many patients had a BMI above the normal
range. BMI, body mass index.

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Evaluation of a noise reduction procedure
Acknowledgments: The authors wish to thank Mr. Akira Hagiwaraof (Foundation) Kanagawa Prefectural Association of Preventive
Medicine, Japan, for providing the reference of BRH method. We
are also indebted to Mr. Yoshiharu Hirata of Division of ClinicalRadiology, Tottori University Hospital, Japan, for helpful discussions and assistance to this study.

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9 Thijssen MAO, Bijkerk KR. Manual contrast-detail phantom
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The authors declare no conflict of interest.
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