COMPUTER-ASSISTED PSEUDO-COLORING METHOD OF LUNG X-RAYS

The 10 Annual International Conference on Computer Science and Education in Computer Science,

July 04-07 2014, Albena, Bulgaria

COMPUTER-ASSISTED PSEUDO-COLORING

METHOD OF LUNG X-RAYS

Dessislava Valentinova GEORGIEVA, Vesselin Evgueniev GUEORGUIEV

Abstract: The purpose of computer-assisted methods is to subserve the

diagnosing process through the presentation of information in the

images more accessible and more informative form. For this purpose,

methods have been developed that take into account the peculiarities of

the human visual system to perceive more easily information in color.

The article described a new method for the pseudo coloring of chest X-

ray images in order to more easily diagnose seals in the lung.

Keywords: X-ray imaging, pseudo coloring, computer-assisted method.

ACM Classification Keywords: medical imaging, computer-assisted methods,

X-ray post-processing, pseudo-coloring

2 D. Georgieva, V. Gueorguiev

Computer-Assisted Pseudo-Coloring Method of Lung X-Rays

Introduction

The use of up-to-date technologies is of crucial importance for

telemedicine. Video as a kind of data and information exchange and/or

communication has an important role in videoconference and

telepresence [Ghaye, 2007].

Interpretation errors of medical images date back to the early days of

Radiology. Medical problem after an interpretation error was first

reported by Garland in 1959. Regardless of changes made, errors

continue to be a major problem as they tend to be very frequent,

especially in early diagnosis of diseases using lung X-rays (around 20-

50% misdiagnosis), and mammography (up to 75% misdiagnosis).

Studies have shown that interpretation errors of images of confirmed

diagnoses reached ~30%. At the same time, when images of

healthy/sick patients are read by a physician, the percentage of

misdiagnoses increased by another 4% [Berlin, 2007], [Garland, 1959].

Errors in reading X-rays are not only the result of a physician’s

qualification but also of their ability to see structures and details in the

image.

In essence, the process of ‘observing’ is very complex and includes

anatomical, physiological, neuropsychological and psycho-emotional

components. In such a complex process, possible errors are not a

surprise, although not all of the images are equally complex. For

example, it is assumed by intuition that bringing an image closer to the

eye results in easier detection of characteristic elements and easier

evaluation of the image. This is not true for images with low contrast,

such as most X-ray images. Visualization and examination of digital

images on the display differs because classical X-ray examination

process cannot be replicated in fullness due to the peculiarities of

imaging systems and the characteristics of human vision. This

determines the continued search for more efficient ways and more

CSECS 2014, July 04-07 2014, Albena, Bulgaria 3

accessible forms of presenting information. An example of this is the use

of color in the preview of PET [PET/CT, 2014] or CT images (Figure 1).

a) b) c)

Figure 1 Examples of color usage in medical images (source: [Stomp, 2012],

[GEHealthcare 1, 2012], [GEHealthcare 2, 2012]).

Human Psychological Color Perception Peculiarities

Color perception is a specific psycho-physiological process that occurs in

the human brain. This process involves psychological factors related to

the way in which color perception is processed by the brain, as well as

physiological factors associated with the ability of the eyes to perceive

light energy.

The perception of colors and color combinations has its own

peculiarities. Most often cited physiological principles in technical

literature are those described by Murch [College, 2000]. This allows for

the creation of a new method based on coloring images were parameters

are set to match the specific application. In this method the most

important characteristics of color perception that carry additional

information are as follows:

Simultaneous usage of highly saturated colors causes fatigue to

the eyes because of the rapid change in eyes lens for their

perception. Hence, the use of not so pure colors or other

selection, which is spectrally close, in some cases is a better

choice;

Details with clean blue color, especially thin lines and small size

objects are difficult to detect. This is due to the lack of cones in

the center of the retina responsible for the perception of the



blue color. In contrast, the blue color is better to use for the

background, particularly in computer displays where it leads to

a blurring of the raster lines in its perception;

The use of similar colors that differ only by the amount of blue

creates a sense of fuzzy edges, as the blue color does not

contribute to the perception of brightness;

The perceptible color change is different in different parts of the

spectrum;

It is difficult for human eye to focus and perceive information

when a change of color is used only in the adjacent areas;

Red and green in the periphery of large screens are not

perceived correctly so they should be avoided there;

Human vision perceives better a combination of contrasting

colors;

Visualization of contiguous areas with pure blue and pure red

color on the display leads to their perception as separate layers

with different depths;

When viewing on display adjacent areas with pure blue and

pure red colors they are perceived as being on separate layers

with different depths.

It has been found that the degree of light is a characteristic, which is

particularly suitable when forming a color scale because it provides for

an arrangement that is based on natural impression. Color scales with

variable intensity are also used, but they are not as effective as others.

Nuances of color are not perceived in the same way as the natural order

of the arrangement formed by brightness. Some color schemes, such as

those representing changes due to gradual heating of objects, rely more

on a symbolic meaning rather than perception. Bipolar color models


where two colors vary in intensity and saturation are best suited for color

scales with a central zero point.

The most commonly used structured multi-tone color scale - Rainbow

has the disadvantage that its central colors - yellow and green, are

perceived as too bright by many researchers. Thus the arrangement

according to brightness is in conflict with the arrangement of colors. At

the same time, spectral-based systems such as Rainbow present an

arrangement of colors according to their physical perception; they do not

form a consistent arrangement according to our perceptions, so in most

cases experts disagree about their use for coding quantitative data

[Breslow, 2010].

Studies in the development of the presented method show that as far as

medical purposes are concerned, colors that look different in slight

variations in the background should be avoided because they may be

perceived as different by the observer and the information they carry

may be lost. In addition, different shades of the same color should be

avoided for denoting objects or areas with different characteristics. This

is especially true for the blue color where different shades are very

difficult to distinguish visually and cannot be defined as areas with

different characteristics.

Method of Computer-Assisted Lung X-Ray Diagnosis Through

the Use of Colors

The proposed method of post-processing X-ray images consists in

pseudo-coloring, i.e. using specially defined color scheme upon

grayscale images [GEHealthcare 3, 2012]. The most popular method of

pseudo-coloring is that of using Look-up tables [Lehmann, 1997]. This

kind of tables is set in computer tomography and position-emission

tomography aimed at improving the contrast of tissues under



observation. Unfortunately, the direct application of this method when

pseudo-coloring X-rayed lungs proved to be non-applicable.

Experiments were carried out to evaluate the existing types of coloring.

This allowed doing different analyses on the possibility of replacing the

gray tones in color, which would allow distinguishing the different

conditions of the lungs’ diseases.

Besides assessing lung diagnosis, the conducted analysis shows that

direct gradient model application is not suitable for treating X-ray images

because of considerable loss of medical artefacts. This fact led to the

conducting of a series of experiments for lung pseudo-coloring.

a) b) c) d)

e) f) g) h)

Figure 2 Application of different color scales upon lung X-ray images: a) HOT,

b) JET, c) HSI in warm colors, d) HSI in cold colors, e) RAINBOW,

f) color schemes for CT scanner.

The ability for visual determination of densities in lung tissues is of

special interest in early stages of healing and determining the vital lung

volume of the patient. The presented references show a lack of pseudo-

coloring methods aimed at helping the diagnosis process. Existing

methods rely on position-emission tomography for organ coloring. This

made it necessary to carry out investigations on the application of


methods used in lung treatments. The fastest initial assessment method

was the use of coloring by tables of conformity (Figure 2).

The analysis of different color scales applications used for medical

purposes showed that the obtained X-ray images display very saturated

colors. This contributes to eye fatigue and hampers the diagnosing

process. Such is the case of applying red color upon very large zones,

as in JET and HIS in warm and color colors. At the same time, in many

zones color does not carry information; it even hampers the image

analysis. Such critical zones are those around bones, the heart, under

the diaphragm, especially when the HIS scale in cold colors is applied. In

some cases the visualization of the grid structure of the bronchus is

being lost - as is the case with HSI in warm and cold colors or it is

significantly reduced as is the case with RAINBOW (Figure 2 g)

When assessing two-shade scales like HOT and HSI in cold colors, HOT

proves much better in displaying lung volume as well as visualizing

bronchus grid, which shows that the greater the contrast between the

bronchus and the surrounding lung tissue, the better the results.

When assessing multi-color scales such as the one used in computer

tomography (Figure 2 e, g) it is established that an ambiguity appears in

the visualization with different colors (Figure 2 e).

In general, the assessment of existing scales in lung X-ray studies shows

that they cannot be applied directly and that a new way of presenting

information of lung X-ray images is to be sought.

Lung X-Rays Pseudo-Coloring Method

The following peculiarities of the conducted experiments on the basis of

analysis of results have been determined:

The direct application of a color scale used for other

applications is not suitable because lung visualization has its

own specific features;



Medically reliable X-ray images cannot be achieved only by

means of direct application of the gradient change of colors.

In the proposed method, color is used for informational purposes rather

than as a qualitative characteristic of X-rays. The aim of the method is to

facilitate diagnosis by color separation of tissues of the chest with

different X-ray densities. This combines visualization capacities of two

color scales - greyscale for the visualization of X-rays and color scale for

the visualization of lung tissues.

The core of the method is to clearly visualize lung capacity as well as

tissue densities. It was defined that red color is the most appropriate for

rendering lung zones with the smallest density - they are equivalent to

dark gray or black zones of ordinary grayscale X-rays. These zones are

indicative of a lung’s vital volume and are immediate characteristics of its

functional abilities.

It is crucial to visualize clearly tissue zones with higher densities

especially those with cancer origin. The particularity of such zones is

their reticulated structure that has to be visualized in order to be

diagnosed correctly. This aim determines the importance of the process

functions that have to preserve the X-ray character.

Investigations showed that tissue structures of interest are to be

distinguished from the surrounding environment not only by color, but by

brightness, too.

In addition, the color applied should not be adjacent to the main one (red

color), neither could it be blue because that will create a sense of

stratification (exfoliation). This was the reason why green was chosen

with a gradation of saturation and brightness - it renders correctly tissues

with different densities.

The remaining X-ray elements of the lungs that are not directly

concerned with the assessment of lung capacity are visualized by


grayscale as in most X-ray images. This allows them to remain visual

and be taken account of in the diagnosis process.

a) b) c)

d) e) f)

Figure 3 Intermediate results of function application upon the X-ray image.

On the basis of the investigations conducted a series of filters for image

processing was determined. The specific point here is that each filter is

applied upon the result of the application of the previous one. The new

method works in the following stages:

The image is transformed into grayscale;

The image is transformed into a negative and its contrast is

increased by extending its histogram;

The process filters are applied upon that negative image aiming

at the 'exfoliation' of the image depending on its X-ray density

and integrating the chosen colors;

A function is applied aiming at restoration of the part of the

image that is not of immediate interest - bones surrounding

tissue environment;

Correction of brightness and contrast of the final image.



The peculiarity of X-ray images is their diverse level of quality (most of

them are underexposed). Therefore each of them is unique in

characteristic (Figure 3 a). Better results are obtained when a method

improving X-ray quality and contrast is applied although this is not

obligatory. The critical point is the convergence of prime X-ray images

into grayscale, thus avoiding undesired coloring during post-treatment

when colors are superimposed. This particularity predefines the

importance of the chosen method of transforming the image from colorful

into greyscale one. Depending on the chosen method, pixels are given

different gray values, which influence the next processing step in

accordance with pixel values.

The second stage of the method consists of transforming the image into

a negative. This includes correcting contrast through different methods -

changing global brightness and contrast, changing on the basis of

gamma-correction, etc. (Figure 3 b). A list of filters follows:

Filter on the basis of applying red color to pixel values while

preserving their brightness value (Figure 3 c);

Gradient filter that enhances the difference in pixel brightness

while maintaining minimum pixel value. This filter visualizes

bronchi's grid structure. The degree of a parameter is chosen to

have 45% of influence (Figure 3 d);

Filter based on gradient change - assigning an additional green

color over the grid structure of the bronchus with middle X-ray

density and extra red upon tissue zones with minimal X-ray

density (air in lungs and tissues exhibits minimal density value).

The aims of this filter are two - to help visualize lungs’ vital

volume (red colored) and to highlight thickened zones

(Figure 3 e).

The third stage of the method brings back pixel values of the input image

to a degree depending on their brightness. What characterizes this stage


is that black color pixels are fully restored to their original values while

lighter-colored pixels are restored according to the degree of their

brightness. This is how visualization of bones and surrounding tissue is

achieved (Figure 3 f).

The fourth stage of filter application, based on gamma-correction

increases the image contrast (Figure 4 b).

а) b)

Figure 4 X-ray image: a) initial unprocessed image; b) X-ray processed with

pseudo-color according to the proposed method.

Conclusion

The application of the proposed new method of X-ray pseudo-coloring

aimed at better visualization of local densities in lungs works when

applied upon X-rays with normal quality. The processed images display

clearly normally functioning lungs as well as higher density zones in the

lungs (Figure 5).

At the same time, underexposed X-ray images have excellent

visualization qualities after being processed with the proposed method.

To achieve that, brightness and contrast are corrected by maximum

during the last stage - histogram is extended and pixels are assigned a

twice as smaller value (after gamma-correction) in comparison with the

standard one. (Figure 6).



Figure 5 X-ray images with normal quality: original X-ray images (left) and

pseudo-colored X-ray images (right).

Figure 6 Processing underexposed X-ray images in the proposed method:

original X-ray image (left); pseudo-colored X-ray image (right).


When processing overexposed X-ray images, lung’s vital volume is

clearly visible while middle level densities in the lung are not clearly

displayed because of lack of information in the original image (Figure 7).

Figure 7 Processing overexposed X-ray images through the proposed method:

original image (left), pseudo-colored X-ray image (right).

References

[Ghaye, 2007] B.Ghaye, J.-F.Monville. Dual Source CT - Perfusion Defect

Visualization with Spiral Dual Energy Scanning. Department of Medical

Imaging University Hospital of Liège Belgium, Updated 12.12.2007

http://www.healthcare.siemens.com/computed-tomography/case-

studies/perfusion-defect-visualization-with-spiral-dual, available: 17.11.2013

[College, 2000] College of Engineering Oregon State University. Guidelines for

Using Color Effectively, Updated 2000, url: http://web.engr.oregonstate.edu/

~pancake/cs252/guide_color.html, available: 21.12.2013



[Berlin, 2007] L.Berlin. Accuracy of Diagnostic Procedures: Has It Improved

Over the Past Five Decades?. In: American Journal of Roentgenology, vol.

188, no. 5, pp. 1173-1178, 2007

[Breslow, 2010] L.Breslow, J.Trafton, J.McCurry, R.Ratwani. An Algorithm for

Generating Color Scales for Both Categorical and Ordinal Coding. In:

COLOR research and application, vol. 35, no. 1, 2010

[Garland, 1959] L.Garland. Studies on the accuracy of diagnostic procedures.

In: American Journal of Roentgenology, vol. 82, pp. 25-38, 1959

[Lehmann, 1997] T.Lehmann, A.Kaserb, R.Repgesa. A simple parametric

equation for pseudocoloring grey scale images keeping their original

brightness progression. In: Image and Vision Computing, vol. 15, pp. 251-

257, 1997

[Stomp, 2012] W.Stomp. Siemens Biograph mCT PET-CT System Receives

FDA Clearance, Updated 16.02.2012. Url: http://www.medgadget.com/2012/

02/siemens-biograph-mct-pet-ct-system-receives-fda-clearance.html,

available: 21.11.2013

[GEHealthcare 1, 2012] GEHealthcare. CT Perfusion 4D Neuro. Updated 2012.

Url: http://www3.gehealthcare.com/en/Products/Categories/Advanced_

Visualization/Applications/CT_Perfusion_4D_Neuro, available: 21.11.2013

[GEHealthcare 2, 2012] GE Healthcare: Integrated Registration. Updated 2012.

Url: http://www3.gehealthcare.com/en/Products/Categories/Advanced_

Visualization/Applications/Integrated_Registration , available: 21.11.2013

[GEHealthcare 3, 2012] GEHealthcare. Spectral Imaging in clinical practice,

Updated 2012. Url: http://www3.gehealthcare.com/~/media/Downloads/us/

Product/Product-Categories/Advanced-Visualization/GSI-Viewer/GEHC-

Case-Study-GSI_Spectral_Imaging_in_Clinical_Practice.pdf, available:

21.11.2013

[PET/CT, 2014] PET/CT. Updated 2014 Url: http://www.citrus

diagnosticcenter.com/petct.htm, available: 21.11.2013

http://www3.gehealthcare.com/en/Products/Categories/Advanced_%0bVisualization/Applications/CT_Perfusion_4D_Neuro

http://www3.gehealthcare.com/en/Products/Categories/Advanced_%0bVisualization/Applications/CT_Perfusion_4D_Neuro

http://www3.gehealthcare.com/en/Products/Categories/Advanced_%0bVisualization/Applications/Integrated_Registration

http://www3.gehealthcare.com/en/Products/Categories/Advanced_%0bVisualization/Applications/Integrated_Registration

http://www3.gehealthcare.com/~/media/Downloads/us/%0bProduct/Product-Categories/Advanced-Visualization/GSI-Viewer/GEHC-Case-Study-GSI_Spectral_Imaging_in_Clinical_Practice.pdf




Authors' Information

Desislava GEORGIEVA, PhD, assistant-professor, New Bulgarian University Sofia, Bulgaria, [email protected].

Major Fields of Scientific Research: 3D computer graphics,

medical imaging, 3D computer animation.

Vesselin GUEORGUIEV, PhD, assistant-professor, Technical University Sofia, Bulgaria, [email protected].

Major Fields of Scientific Research: embedded systems, real-

time systems, validation and verification of software applications,

medical imaging, 3D computer graphics, 3D computer animation.

mailto:[email protected]

COMPUTER-ASSISTED PSEUDO-COLORING METHOD OF LUNG X-RAYS

Documents

Transcript of COMPUTER-ASSISTED PSEUDO-COLORING METHOD OF LUNG X-RAYS