H. Badioze Zaman et al. (Eds.): IVIC 2013, LNCS 8237, pp. 165–175, 2013. © Springer International Publishing Switzerland 2013

Segmentation of the Lung Anatomy for High Resolution Computed Tomography (HRCT)

Thorax Images

Norliza Mohd Noor1, Omar Mohd Rijal2, Joel Than Chia Ming1, Faizol Ahmad Roseli2, Hossien Ebrahimian2, Rosminah M. Kassim3, and Ashari Yunus4

1 UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

2 Institute of Mathematical Sciences, University of Malaya, Kuala Lumpur, Malaysia 3 Department of Diagnostic Imaging, Kuala Lumpur Hospital, Malaysia

4 Institute of Respiratory Medicine, Malaysia norliza@utm.my

Abstract. In diagnosing interstitial lung disease (ILD) using HRCT Thorax images, the radiologists required to view large volume of images (30 slices scanned at 10 mm interval or 300 slices scanned at 1 mm interval). However, in the development of scoring index to assess the severity of the disease, viewing 3 to 5 slices at the predetermined levels of the lung is suffice for the radiologist. To develop an algorithm to determine the severity of the ILD, it is important for the computer aided system to capture the main anatomy of the chest, namely the lung and heart at these 5 predetermined levels. In this paper, an automatic segmentation algorithm is proposed to obtain the shape of the heart and lung. In determine the quality of the segmentation, ground truth or manual tracing of the lung and heart boundary done by senior radiologist was compared with the result from the proposed automatic segmentation. This paper discussed five segmentation quality measurements that are used to measure the performance of the proposed segmentation algorithm, namely, the volume overlap error rate (VOE), relative volumetric agreement (RVA), average symmetric surface distance (ASSD), root mean square surface distance (RMSD) and Hausdorff distance (HD). The results showed that the proposed segmentation algorithm produced good quality segmentation for both right and left lung and may be used in the development of computer aided system application.

Keywords: Interstitial lung disease, high resolution computed tomography (HRCT), segmentation quality.

1 Introduction

Interstitial lung disease (ILD) is gaining more attention due to the increasing cases reported worldwide. Although it covers only 15% of the recognized pulmonary diseases [1], issues such as the low survival rate [2] leads to the deeper study on the disease. ILD is a progressive disease which is only can be controlled or stagnated.

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It will cause the reduction of lung volume hence affect the lung capability and patient physical ability as well. Severe cases had been reported to cause lung collapse and mortality [3]. Current practice on the ILD diagnosis includes patient history, physical examination, radiologists’ findings and lung biopsy.

HRCT thorax is the most common imaging modalities used in the identification of any lung pathology due to its capability to produce a stack of very thin slices of images [4]. Investigating the pathology signs in the image can identify the presence of disease, the disease pattern and severity, which is crucial for medication and therapy planning. Different sub-category of ILD has different pattern/features in the HRCT image.

Modern HRCT scanner capable to produced up to 300 slices for single patient. The enormous number of images to be observed by radiologist costs a lot of time, which later may lead to other human error in reporting the case. In addition to that, different radiologists may have different opinion on the same set of images based on their experiences and unique judgment. To reduce time consumption and lower the human error risk, together to avoid inconsistency among different radiologists, a scoring index has been proposed in recent years. The scoring index requires the radiologists to investigate several changes or features which indicates the presence of ILD in only few selected slices instead of the whole set of image. The slices are selected based on the lung anatomy, representing the entire lung area from top (apices) to the bottom (diaphragm). In this study, we used five slices at predetermined levels based on our consultation with the experienced radiologists. The slices are taken and labeled as below;

i. Level 1 (L1) – aortic arch

ii. Level 2 (L2) – trachea carina

iii. Level 3 (L3) – pulmonary hilar

iv. Level 4 (L4) – pulmonary venous confluence

v. Level 5 (L5) – 1-2cm above right hemi-diaphragm

Segmenting body anatomy is increasingly popular and employed for different subjective purposes. Previous studies showed that the segmentation of the specific organs or tissues mainly contributes in diagnosing and monitoring the presence of any disease, and also complement the surgery preparation [5], [6]. The variations of the anatomy among different cases due to the intersections of different structures and pathological abnormalities, limit the performance of an algorithm [7].

Generally, a segmentation procedure can be divided into two, region-based and contour-based methods. Region-based approach deals directly with the local information of an object, such as texture and gray levels. It includes thresholding, filtering and morphological operation. Contour-based segmentation utilizes the boundary representation of an object, and further processes are based on the contour manipulation. Current trend on segmentation algorithm have seen that the combinations of both approaches are preferred. For a fully automated system, the model-based procedure with the combination of statistical shape model and some deformation segmentation yields optimal results [8], [9],10].

Segmentation of the Lung Anatomy for HRCT Thorax Images 167

This study is motivated by the work of Lim, Jeong and Ho [11] who has proposed an algorithm which make use of the multilevel thresholding and morphology operation to obtain a search range of an object’s shape, followed by the gradient-label mapping and labeling-based search algorithm to get the final object region.

In developing an algorithm to assess the severity of the ILD, it is important for computer aided system to be able to capture the main anatomy of the chest, namely the right lung, the left lung and the heart. In this paper a segmentation algorithm to detect the three main anatomy of the lung is proposed. The results from the proposed segmentation algorithm are then compared with the ground truth images prepared by senior radiologist to measure the segmentation quality.

2 Data Collection

HRCT Thorax images of 96 patients were collected from the Department of Diagnostic Imaging of Kuala Lumpur Hospital that consists of 15 healthy individuals (normal cases), 28 ILD cases and 53 other lung related diseases (non-ILD). The ILD cases were not restricted to any specific subcategory disease. The images were scanned using Siemens SomatomPlus4 CT scanner, and observed by the respective radiologists by Syngo Fast View version VX57G27. Each slice was obtained at 10 – 30 mm intervals of patients in supine position will full suspended inspiration.

The segmentation algorithm was developed using the normal cases and tested with all cases combined. A senior radiologist did the manual tracing of the heart, right lung and left lung on ten patients using level 5 image slice picked at random from the 96 patients to produce the ground truth images.

3 Methodology

The procedure begins with the segmentation of the lung and the heart, which is motivated by the approach presented in [12]. It involved global thresholding, morphology operation and connected-component analysis. The quality of the segmentation results is measured via 5 supervised evaluation methods, which were inspired by [6]. These methods compared the segmentation results with the ground truth images which were manually segmented by the experience radiologists.

3.1 Lung and Airway Segmentation

The flow chart of the proposed lung segmentation algorithm is shown in Figure 1. The Otsu thresholding was applied in order to segment the lung area based on the intensity level. Lung tissue is characterized as having darker pixel (HU value equal to -1000). Otsu thresholding assigned the group of dark pixels to 0 and others to 1. This output was inverted so that the 0 pixels become 1 and vice versa. It is important to highlight that the dark pixels is our reference pixel while the brighter pixels are the unwanted area.

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Next, any area that consists of less than 700 pixels was removed assuming that the lung regions are large. Morphology closing is applied to the remaining regions so that any adjacent region is connected. This step aims to regroup pixels that belong to the same tissue/organ/anatomy which were separated by the previous thresholding step.

Connected component analysis was applied to the remaining regions via 8-neighbourhood approach. The labeled regions consist of several areas including non-lung regions such as the background outside the patient’s body. To extract the lung regions, another rule were defined as follow;

Rule 1. Labeled components attached to the image frame are assumed to be the background.

Rule 2. Lung region consists of the two largest labeled components in the image. For each region, area and eccentricity measure (Ecc) were calculated. The regions that have the two largest area and eccentricity measure of

9.0Ecc5.0 << were selected as right lung and left lung.

Morphology dilation was employed to the extracted lung region in order to regain as accurate as possible the original shape and size of the lung. Finally, any holes present in the lung area were filled through flood-fill algorithm.

To decide which is right and left lung, the location of the centroid of each segmented lung regions were observed. The one having centroid with lower value in x-axis of the image is assumed to be the right lung while the other is the left lung. The output of the segmented right lung and left lung images were stored separately as bitmap file.

3.2 Heart Segmentation

Using similar approach to segment the lung, the heart area located between right and left lung (approximately in the middle of the HRCT image) was segmented. However, Otsu thresholding cannot separate the heart region due to its intensity similarity to other surrounding tissues. Thus, manual thresholding was incorporated where the histogram of the image was studied to obtain the range of intensity that constitutes the heart region.

Morphology closing operation was implemented to the threshold image to close any gap between pixels and erosion process was applied to separate connected regions. Any holes present in the remaining regions were filled similar as in section 3.1.

Next, the largest labeled component was extracted by assuming that the largest component represented are the heart region. Morphology dilation was applied in order to regain as close as possible the original shape and size of the heart area. Figure 2 shows the flow chart for heart segmentation algorithm. The output of the segmented heart images is also stored as bitmap file.

3.3 Ground Truth

A system to capture the manual tracing done by the radiologist was developed in MATLAB GUI environment. The result of the radiologist manual tracing is in bitmap file and named as ground truth image.

Segme

Fig. 1. T

entation of the Lung Anatomy for HRCT Thorax Images

The segmentation of the right and left lung

False

False

Eliminate

Eliminate

169

170 N.M. Noor et al.

Fig. 2. F

4 Segmentation Q

The segmentation quality ground-truth image. The exradiologist) manually definstandard) by manually tracecomparison was measured

Flow chart for heart segmentation algorithm

Quality

is measured by comparing the segmented image to xpert in the respective field (in this study will be the sennes the ground-truth or reference image (some called ged the boundary for each lung and the heart anatomy. Td and the scores are given according to the detec

the nior gold The cted

Segmentation of the Lung Anatomy for HRCT Thorax Images 171

deviations. Even though there are many measures that can be used in the evaluation, in this study, five metrics were considered as follows [6];

i) Volume Overlap Error rate (VOE)

Volume overlap rate is defined as the ratio of intersection to the union between the segmented region and the reference region (ground truth or gold standard). It is also known as Jaccard or Tanimoto coefficient. The overlap error rate is defined as in (1). Perfect segmentation leads to a 0% score while 100% error rate means there is no overlap at all.

Volume Overlap Error rate = %1001 xBA

BA

∪∩− (1)

where A is the segmentation result and B is the reference image.

ii) Relative Volumetric Agreement (RVA)

This metric measures the area similarity between segmentation result and the reference region. It evaluates the similarity in size between both regions. The larger value of agreement indicates that the higher similarity of size between the segmented region and the gold standard

iii) Average Symmetric Surface Distance (ASSD)

It is used to assess the accuracy of the segmented shape corresponds to the reference region. The average distance of all contour points of the segmented shape to the closest contour point of the reference region is calculated. The smaller value denotes the higher similarity between both regions. It is also known as mean absolute distance (MAD).

Assuming that S(A) represents the set of surface pixels of A and S(B) denotes the surface pixels of B, the average symmetric surface distance (ASSD) is given by;

( ) ( )( ) ( )( )( )( )

+=

∈ ∈ASs BSsBA

A B

ASsdBSsdkBAASSD ,,,

where ( ) ( )BSASk

+= 1

and d sA, S B( )( ) = minsB ∈S B( )

sA − sB denotes the shortest distance of an arbitrary

voxel, sA to S(B) and .indicates the Euclidean distance measure. Notice that the same process is repeated from voxel sB to S(A) to provide symmetry analysis.

172 N.M. Noor et al.

iv) Root Mean Square Surface Distance (RMSD)

Similar with ASD, however, the Euclidean Distance between both S(A) and S(B) (

d cA,C B( )( ) and d cB,C A( )( ) ) are squared before being stored. Eq.5 is modified

as follows;

( ) ( ) ( ) ( )( ) ( )( )( )( )

+×

+=

∈ ∈ASs BSsBA

A B

ASsdBSsdBSAS

BARMSD ,,1

, 22

where the definition hold the same as in ASD.

v) Hausdorff Distance (HD)

This metric measure how different of two images to each other, and is also known as Hausdorff distances. Similar to ASD and RMSD, MSD considers only the contour pixels of each set. General expression of the Hausdorff distance, or in our case, known as MSD, is shown as follow;

( )( )

( )( )( )

( )( )

=

∈∈ASsdBSsdBAMSD B

BSsA

ASs BA

,max,,maxmax,

Note that the distance of both directions, ( )( )BSsd A , and ( )( )ASsd B , are considered to provide symmetry analysis.

5 Results and Discussion

Figure 3, Figure 4, and Figure 5 show some sample the results of segmentation of the heart, right lung and the left lung using the proposed method for normal lung, ILD and non-ILD HRCT Thorax images. Based on visual inspection, both lung were successfully segmented for all 5 predetermined levels, however the same cannot be said for heart segmentation. In determine the segmentation quality of the proposed algorithm five metrics as proposed in Section 4 were used in this study. The segmented image of the heart, right lung and the left lung are compared one to one with the ground truth images of the same patient's anatomy. Five measurements of segmentation quality was derived and shown in Table 1 for heart, right lung, and left lung.VOE, ASSD, RMSD and HD metrics should have low value for segmentation to have good quality. RVA metric on the other hand defined that high value represent higher similarity. Results shown in Table 1 show that right lung and left lung have lower value than heart segmentation for VOE, ASSD, RMSD and HD and higher value for RVA implying that the segmentation for right lung and left lung has higher quality than the segmentation result for the heart boundary. The findings correspond to the visual inspection done by the radiologist. Further work need to be done to improve the segmentation quality for heart.

Segmentation of the Lung Anatomy for HRCT Thorax Images 173

(a) Level 1-aortic arch (b) Level 2-trachea carina (c) Level 3-pulmonary hila

(d) Level 4-pulmonary venous confluence e) Level 5-1-2 cm above right hemi-diaphragm

Fig. 3. Segmented image superimposed with the original image for five levels (a) - (e) of HRCT Thorax for ILD cases

(a) Level 1-aortic arch (b) Level 2-trachea carina (c) Level 3-pulmonary hilar

(d) Level 4-pulmonary venous confluence (e) Level 5-1-2 cm above right hemi-diaphragm

Fig. 4. Segmented image superimposed with the original image for five levels (a) - (e) of HRCT Thorax for non-ILD cases

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(a) Level 1-aortic arch (b) Level 2-trachea carina (c) Level 3-pulmonary hilar

(d) Level 4-pulmonary venous confluence (e) Level 5-1-2 cm above right hemi-diaphragm

Fig. 5. Segmented image superimposed with the original image for five levels (a) - (e) of HRCT Thorax for normal cases

Table 1. Results of segmentation quality measures for heart, right lung and left lung

Heart Right Lung (RL) Left Lung (LL)

Heart Mean Variance STD Mean Variance STD Mean Variance STD

VOE 18.75 25.35 5.04 6.73 64.42 8.03 10.56 87.23 9.34

RVA -12.19 70.73 8.41 2.62 80.99 9.00 4.31 111.95 10.58

ASSD 7.68 5.42 2.33 2.99 10.66 3.27 3.26 3.97 1.99

RMSD 141.33 5359.91 73.21 40.70 20885.22 144.52 30.54 3867.06 62.19

HD 40.61 127.98 11.31 10.19 155.76 12.48 13.97 234.95 15.33

6 Conclusion

Five segmentation quality measurements that are the volume overlap error rate (VOE), relative volumetric agreement (RVA), average symmetric surface distance (ASSD), root mean square surface distance (RMSD) and Hausdorff distance (HD) were used to determine the segmentation quality for the lung anatomy. The results showed that the proposed segmentation algorithm produced good quality segmentation for right and left lung and may be used in the development of computer aided system application. However, further work need to be done to improve the segmentation quality for the heart.

Segmentation of the Lung Anatomy for HRCT Thorax Images 175

Acknowledgement. We acknowledge the contribution from Dato’ Dr. Abdul Razak bin Abdul Muttalif, The Insitute of Respiratory Medicine, Kuala Lumpur, and Dr. Hajjah Zaleha binti Abd. Manaf, Head of Radiology Department (Diagnostic Imaging), Kuala Lumpur Hospital. This research is funded under UTM research grant (GUP 00H43).

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