AUTOMATED DETECTION OF SUPPORTING DEVICE

POSITIONING IN RADIOGRAPHY

Chen Sheng, Li Li and Ying Jun

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

Keywords: Gaussian Filter, Contrast-Limited Adaptive Histogram Equalization (CLAHE), Hough Transform, Tube.

Abstract: Portable X-ray radiographs are heavily used in the ICU for detecting significant or unexpected conditions

requiring immediate changes in patient management. One concern for effective patient management relates

to the ability to detect the proper positioning of tubes that have been inserted into the patient. These include,

for example, endo-tracheal tubes (ET), feeding tubes (FT), naso-gastric tubes (NT), and other tubes. Proper

tube positioning can help to ensure delivery or disposal of liquids and air/gases to and from the patient

during a treatment procedure. Improper tube positioning can cause patient discomfort, render a treatment

ineffective, or can even be life-threatening. However, because the poor image quality in portable AP X-ray

images due to the variability in patients, apparatus positioning, and X-ray exposure, it is often difficult for

clinicians to visually detect the position of tube tips. Thus, there is a need for detecting and identifying tube

position and type to assist clinicians. The purpose of this paper is to present a computer-aided method for

automated detection of tubes and identification of tube types. Use of this method may allow clinicians to

detect the tube tips more easily and accurately, thus improving the quality of patient management in the

ICU.

1 INTRODUCTION

Computer-aided diagnosis is designed to help

physicians improve the diagnostic accuracy of

radiological images and for detection of the disease,

and to explain the consistency, reduce the rate of

misdiagnosis, and cause less opportunity for eye

fatigue

. The chest CAD system (Brem and Baum,

2003) and the Mammography CAD system (Bram

and Bart, 2001) are both used in clinics. Clinical

results show two aspects: Medical diagnostic

radiology consults the CAD output and it is thus

easier to find more features, such as micro-

calcifications and the changes that have taken place

in the tiny structures, greatly improving the

efficiency and accuracy of diagnosis. We research

the method of tube automatic detection for

improving the quality of patient management in the

Intensive Care Unit (ICU) (Doi and MacMahon,

1999).

ICU patients, particularly those with heart and

lung diseases, rely on the existence of tubes to live

and be treated. In the intensive care setting, catheters,

tubes, and monitoring devices play an important role.

Proper placement of these devices is crucial to their

function Personnel are well aware of the need for

timely medical ICU care for patients, correct

placement of tubes, and the changes that need to be

made around these tubes’ positions. If the computer

can automatically identify the location of tubes and

their tips, and enhance medical images around tubes

to provide diagnosis, it is a clear and very important

improvement to their procedures.

ICU patients’ chest X-ray images can be fuzzy,

exhibit low contrast and noise, and contain many

different types of tube connections on the image,

such as the endo-tracheal tube, feeding tube, naso-

gastric tube, pulmonary artery, central venous

catheter, and other catheters required for the

treatment of a variety of medical conditions. These

bring a significant challenge to accurately detect

tubes and their tips. Figure 1 shows a general

original ICU chest image.

2 METHODS AND MATERIALS

We collected a database consisting of 107 portable

X-ray images from 20 patients using Kodak’s

computed radiography (CR) system. An experienced

420

Sheng C., Li L. and Jun Y. (2008).

AUTOMATED DETECTION OF SUPPORTING DEVICE POSITIONING IN RADIOGRAPHY.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 420-424

DOI: 10.5220/0001061004200424

 SciTePress

Figure 1: Original ICU chest X-ray image.

chest radiologist reviewed all the images from the 20

patients and provided a diagnosis for each image

including the types of tubes and locations of their

tips. The technique we developed here was

evaluated for the detection of the three commonly

used tubes in the ICU, the endo-tracheal tube, the

feeding tube, and the naso-gastric tube. In this

database, 33 images were identified to have endo-

tracheal tubes, 54 with feeding tubes, and 22 with

naso-gastric tubes. This technique will be used and

evaluated for the detection of other tubes/lines in the

future.

Figure 2 lists the steps used in the automated

detection method. In the image-processing step, the

contrast-limited adaptive histogram equalization

(CLAHE) (Pizer and Amburn, 1987) (Zuiderveld) is

used to enhance the contrast, and the anisotropic

filtering is used to remove the noise prior to the

generation of a gradient image. CLAHE can enhance

the contrast details of the regions and avoid noise

amplification as a result of histogram equalization in

a similar region. As with the general histogram

equalization, which can change the grey scale of the

image to enhance the contrast, its distinction is that

the operation region is a small region from which the

whole image is divided, and then merged together

again as the whole image and using bilinear

interpolation between two neighbourhood

intercropping to eliminate false results of the border

reduced by histogram equalization. The combination

of a canny filter (Parker, 1997 and Canny, 1986) and

Hough transform (Kamat and Ganesan, 1998) is then

applied to detect edges and lines on the tiles of an

enhanced gradient image. A whole gradient image is

divided into many tiles for performing Hough

transform. The tube in a small tile can be considered

a straight tube. The double-line/edge criteria are

applied to identify potential tube candidates by

paring a detected “left” edge with a “right” edge

(See Fig.3). Theoretically, the paired left and right

edges should have a fixed distance between them

and each should have a gradient with an opposite

direction (i.e., G, -G). Therefore, tubes’ edges

should be basically parallel so it can be determined

which tube is the valid one (See Fig. 4). Further, we

apply bilateral Hough transform to detect the

missing lines between potential tube candidates.

Figure 2: Tubes’ automatic detection flowchart.

Figure 3: Tube matching.

Figure 4: Determine the valid tube.

Note: SB is the abbreviation of Space Between.

SBSD is the abbreviation of Space Between

Standard Deviation. ABS is the average of SB.

AUTOMATED DETECTION OF SUPPORTING DEVICE POSITIONING IN RADIOGRAPHY

421

Figure 5: Bilateral Hough transform.

Figure 5 shows the detected missing/lost lines

between the two potential tubes identified in the

previous step. Bilateral HT: Using the tube’s grey

gradient (left is Gx and right is –Gx) to do bilateral

Hough Transform. The paired left and right edges

should have a fixed distance between the two edges

and each should have a gradient with an opposite

direction. The detected tube’s size is assumed as the

distance. Basing the detected tube’s position, we do

the bilateral Hough Transform to gradient image

from top to bottom, then from bottom to top. After

doing the bilateral Hough transform, we can locate

the missing tubes.

In a small tile each to be linked with at least the

boundary line is another small tile. In other words,

the starting point of the boundary line and the end

point of another boundary line must be in one pair of

neighbouring tiles. The connective tubes’ directive

angle difference should be in pi/24. The tubes on the

images are consistent. When detecting ET, only the

isolated tube in the region of interest (ROI) upper

part is valid. If a tube can connect with more than

two tubes, we will choose the tube that bears a closer

directive angle. See Fig. 6.

Tip detection is an important element of our

work. Combining the region’s information and

anatomic structure, we use our algorithm (See Fig.

7). The tubes’ edges should be crossing or the tube

size should be less than the defined size (i.e., ET,

NT: 3-10 pixels， FT: 15-25 pixels). (See Fig. 7:

Case 1-2). We use the proper bilateral Hough

Transform to stretch or shrink the tube for getting an

accurate tip (See Fig. 7: Case 3).

Figure 6: Tubes’ connection.

Figure 7: Tip detection.

After determining the tip, a classification step is

executed to provide a decision on apparent tube type

for the matched pairs of left and right edges.

Information such as the length of the line, the

location of the tube, and/or the tip relative to

anatomic structures is used for classifying the tube

types. The ROIs containing relevant anatomic

structures, such as lung, mediastinum, and stomach,

are identified and used to determine the relative

position of tubes in the image. These ROIs serve as

landmarks for tube detection and classification.

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422

3 RESULTS

The detection result can be illuminated. See Fig.8-

12. We evaluated the performance of the technique

for ET, FT, and NT. Our preliminary results showed

that use of the presented technique correctly detected

the location for 94% of the 33 ET tubes, 82% of the

54 FT tubes, and 82% of the 22 NT tubes with no

false positive detection (See Table 1). The

performance is expected to improve when detection

results from the same patient are used.

Figure 8: The original X-ray chest image.

Figure 9: Image pre-processing (CLAHE)

Figure 10: ET detection

Figure 11: FT detection.

Figure 12: NT detection

Table 1: The result table.

107 images of 20 ICU patients, and the images were captured

by portable CR system of Kodak

ET FT NT

Tube amount 33 54 22

Detection

rate

94% 82% 82%

4 CONCLUSIONS

Our novel detection technique can accurately detect

the tubes in ICU images at a high sensitivity level. A

function of automated detection of tube placement

can potentially improve the overall workflow and

patient management in the ICU.

ACKNOWLEDGEMENTS

This paper is supported by Innovation Program of

Shanghai Municipal Education Comission.

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