A IMAGE PROCESSING METHOD FOR COMPARISON OF

MULTIPLE RADIOGRAPHS

Chen Sheng, Li Li and Wang Pei

College of Mathematics and Science, Shanghai Normal University, Guilin Road 100, Shanghai, China

Keywords: Image Processing, Look up table (LUT), Computer-aided diagnosis (CAD).

Abstract: Portable chest radiography is the most commonly ordered radiographic test in the intensive care unit (ICU).

In the ICU, a succession of portable images is usually taken over a period of time to monitor the progress of

a patient’s condition. A prompt diagnosis of any changes in the conditions of these ICU patients allows

clinicians to provide immediate attention and treatments that are required to prevent the conditions from

worsening and which could result in a treat to the patient’s life. However, because of differences in X-ray

exposure setting, patient and apparatus positioning, scattering, and grid application, for example, differences

in image quality from on image to the next taken at different times can be significant. The differences in

image quality make it difficult for clinicians to compare images to detect subtle changes. This paper

presents an image-rendering method that reduces the variability in image appearance and enhances the

diagnostic quality of these images. Use of the presented method allows clinicians to detect subtle

pathological changes from one image to the next, thus improving the quality of patient management in the

ICU.

1 INTRODUCTION

In the ICU, clinical evaluation can rely heavily on

diagnostic images such as portable chest

radiographic images. The successive diagnostic

images taken by a portable computed radiography

(CR) system are helpful for indicating significant

pathological changes of the patient, such as a

collapsed lung or and improper tube placement

within the patient.

However, image differences owing to different

exposure settings, or patient and apparatus

positioning, limit the accuracy of image comparison

in the ICU, even for those images obtained from the

same patient over a short treatment interval.

Obviously it constrains the ability of the clinician to

subtle changes that can be highly significant. An

important problem is allocating the output dynamic

ranges to display the clinically important part of the

input code values. The process of selecting the

relevant sub-range of input code values and

constructing the proper mapping function from the

input code values to the output display media is

termed a tone-scale adjustment. Using a tone-scale

method in CR images provides an optimal rendering

result (Lee and Barski, 1997). There are also other

methods (Barski and Metter, 1998) that provide

improvements in contract enhancement for

diagnostic imaging.

However, these methods do not address the

problem of consistent rendering between images of

the same patient taken at different times. Application

of such tone-scale techniques is not likely to provide

consistent rendering results, which makes accurate

changes assessment by the ICU clinician difficult.

In this paper, we present a region of interest

(ROI)-based lookup table (LUT) mapping method

for diagnostic images that provides a consistent

rendering result for images taken of the same patient

at different times. This will help the clinicians

compare images and track patient progress. First is a

background segmentation step when the background

of all the images (that may have different amounts of

background content or no background content) are

segmented. In the ROI selection step, the ROI region

is located. These are the images of the tissue parts

that are critical for clinicians to make a correct

diagnosis. Next, an LUT constructed for the pixel

values in the ROI. Then a toe-shoulder construction

step is taken, constructing a LUT for very dark and

very light regions. In the LUT mapping step, the

pixel values in the input images are mapped to the

corresponding pixel values in the output image.

228

Sheng C., Li L. and Pei W. (2008).

A IMAGE PROCESSING METHOD FOR COMPARISON OF MULTIPLE RADIOGRAPHS.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 228-231

DOI: 10.5220/0001060902280231

 SciTePress

The structure of this presentation is organized as

follows: in section2, we introduce why and how the

ROI is selected. In section3, the ROI-based LUT

construction method is presented. Section4 reports

the performance comparison result of the current

method and of baseline method. Finally, the

conclusion is drawn in Section 5.

2 AUTOMATED ROI SELECTION

After doing a background segmentation based on

ICU’s image histogram and difference histogram

(Kuhn, 1999), we get an appropriate threshold for

removing the background. A region-labeling

operation can be done to prevent over-segmentation.

Then we perform the automated ROI selection.

In ICU images, the position of the parts

necessary for the clinicians’ diagnosis varies. In

some cased, they will only take up a little part of the

image. The basic principle of automated ROI

selection is to identify the RIO in each image

automatically and adjust the image contrast values

within the ROI to a suitable range for each image, so

that comparison of one image to another is feasible.

Figure 1: Automated region of interest selection; this is an

example of selecting similar regions of interest for two

images of the same patient.

ROI identification located key features (lung line,

spine line) in an image and allows the correlation of

two or more images accordingly. Figures1(c) and (f)

show two chest X-ray images of the same patient

with two automated regions of interest (ROI)

selected.

First we use a median filter to resize the image,

then a Gaussian filter for noise removal. Next, the

locations of the spine line and lung line are detected

(Amit and Mark, 2005). Fig.1 (a) and (d) show the

spine and lung line detection. We search for the

highest/lowest mean column value row by row.

Connecting these points, we validate the lung line

step (Fig.1 (b) and (e)), and combine and validate

similar lung line parts based on gray-level and

position.

With the approximate lung line and spine line

determined, a spine-line-fitting step can be executed.

This is performed by doing an iterative of the spine-

line-fitting step. We search all the rows between the

top and bottom of the lung lines. We then choose the

fitting result that has the lower mean residual form

these two. We then can get a trapezoid ROI for all

the images of the same patient based on the spine

line and the distance of the spine line to the lung

line.

3 ROI-BASED LUT

CONSTRUCTION

Once one or more ROIs have been identified, we can

do the ROI-based LUT construction step.

First we identify the primary area o the image

from the histogram data that is related only to the

ROI. Points lp and rp represent left and right points,

respectively, of the histogram data that is from the

main range (2.5%-95%) in the ROI. After that, for

each image, left points lp1 and lp2, and right points

rp1 and rp2, are obtained. The goal of next few steps

is to remap left points lp1 and lp2, and right points

rp1 and rp2, to the corresponding points A1 and A2,

in order to form consistent images in the output

images.

Figure 2: Lookup table construction.

Figure 2 shows how various portions of the image

are remapped for consistent rendering. We can map

the right point rp, obtained from the ROI of each

input image, to the same value Ar in the output

image that has been determined for the same patient.

However, to accommodate the difference in patient

position between two images of same patient, we

proposed to use Ar for each image. Here, the

A IMAGE PROCESSING METHOD FOR COMPARISON OF MULTIPLE RADIOGRAPHS

229

diaphragm in Fig.1 (a) is higher that that in Fig.1 (d).

This difference can be best expressed by means of a

proportion of distance d to column length Lc for

each image as illustrated in Fig.1.

Given these considerations, Ar can be calculated

using the following calculations to adjust the

difference in patient position:

() ()

101

;;

212211

≤≤−⋅+⋅=

⎟

⎠

⎞

⎜

⎝

⎛

⋅=+

⎟

⎠

⎞

⎜

⎝

⎛

⋅=

ttArtArAr

pArp

pAr

(1)

Where

and column length

are illustrated in

Figure 1.

, pp

and

are empirical parameters.

In out method, features used to determine the

value

include the difference

lprp −

, and the

value of

()

rencespinediffelprp /−

()()()

()()

AlAlAlAl

aapdarkaaAlAl

spuvspdv

lprp

pdark

cbxaxAllprpx

,maxmin,minmax

,min,max

;

4021

−⋅+=

−

++=−=

(2)

AlAlaaaacba max,min,,,,,,,

4021

are empirical

parameters and

spuvspdv, (spine down-part value

and spine up-part value) are the main gray-level

range in the spinal region (10%-80%), which can be

detected automatically. Note that the A1 can be

justified differently by the ratio of

pdark

for each

image. Here we choose the same

Al for all the

images from the same patient.

After we get

ArAlrplp ,,,

for each image, the

LUT construction between

lp and rp to Al and

can be applied. The mapping from

[

]

rplp,

[]

ArAl,

is established based on the active rate (Lee,

2004) calculated in equation (3).

()

() ()

()

,,, 33

,,,

[]

[][]

ln 1 1

[]

[][]

ln 1 1

[]

ij ROIimgij kui v j

ilp

Huvij

Activity k

Activity i t h i t

ActNor i

Act i t h i t

∈==−=−

=−

−

=−

−

⎛⎞

+⋅ +

⎜⎟

⎝⎠

⎛⎞

+⋅ +

⎜⎟

⎝⎠

∑∑∑

∑

() ( ) ()

1, ,,,,,1whenh imgij imguv hrHuvij

−

≤− ≤ =

∑

(3)

][kactivity

is the activity of intensity k and h(i) is the

number of the pixel at that intensity.

Figure 3 shows an ICU image’s active rate and

example of LUT construction using equation(4)

considering the active rate.

Figure 3: Active-rate in LUT construction.

(

)

( ) () ()( )

()()

0,10,

=≤≤

−

−−

−+=+

⎟

⎠

⎞

⎜

⎝

⎛

whenratioratioHere

lprp

ratioAlAr

ratioAlAriActNoriLUTiLUT

AllpLUT

(4)

In addition to mapping the ROI of the image, for

darker or brighter regions, a toe-shoulder LUT

construction step was performed for additional

mapping, such as the toe region and the shoulder

region in the LUT curve in Fig.3. The toe region was

constructed for mapping the dark area in the image

and the shoulder region was constructed for the

bright area in the image.

4 PERFORMANCE

We collected 83 portable X-ray images from 19

patients. There were two to nine images of each

patient. An experienced chest radiologist reviewed

all the images from the 19 patients and provided a

diagnosis that included the types of diseases detected

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

230

and any change in a patient’s condition

(improved/worsened). We compare the presented

method with a baseline image enhancement

technique that is an image optimization technique

based on single image (Barski and Metter, 1998). An

evaluation of the images from the 19 patients was

performed in order to compare the overall

consistency in the image and the lung areas as well

as the ability to detect changes in patients’

conditions against the radiologist’s diagnosis.

Figure 4: Processed image of the same patient. (a), (b),

and (c) are raw images of the same patient, and (d), (e) and

(f) are the processed result using the current method.

Figure 4 shows the processing result examples of a

patient’s ICU chest X-ray images. In the evaluation,

all of the processed images form each patient were

presented to a radiologist in the order of the

processed images form the baseline method first and

next the processed images from the presented

method. The radiologist gave a rating on a 5-point

acceptability scale where 1 is not acceptable and 5 is

outstanding in terms of the consistency rendering

effect demonstrated among the images presented for

diagnostic purposes. Table 1 is the evaluation result.

A t-test is also done to compare the baseline and

current methods.

5 ROI-BASED LUT

CONSTRUCTION

Our image-rendering technique reduces the

variability in the image appearance caused by the

differences in patient or apparatus positioning and

image acquisition parameters. The improved

consistency over the baseline image enhancement

technique can potentially improve the overall

workflow and patient management.

Thus, it is a method for enhancing diagnostic

images taken at different time in order to provide

consistent rendering for regions of interest.

Table 1: The evaluation result.

Score

Patient Number

5 4 3 2 1

Average

T-test

Current

Method

19 9 8 1 1 0 4.3

Baseline

Method

19 2 9 5 3 0 3.5

P-Value

=0.039

Score

Image Number

5 4 3 2 1

Average

T-test

Current

Method

83 42 30 9 2 0 4.3

Baseline

Method

83 27 34 17 4 1 3.9

P-Value

=0.0034

ACKNOWLEDGEMENTS

This paper is supported by Innovation Program of

Shanghai Municipal Education Comission.

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