2012 International Conference on Biomedical Engineering and Biotechnology
Automatic Rib Segmentation in Chest CT Volume Data
Li Zhang
Xiaodong Li
Qingmao Hu
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China The Chinese University of Hong Kong, Hong Kong, China Shenzhen Key Laboratory of Neuro-Psychiatric Modulation, Shenzhen, China
Linyi People’s Hospital, Shandong Province, China
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China The Chinese University of Hong Kong, Hong Kong, China Shenzhen Key Laboratory of Neuro-Psychiatric Modulation, Shenzhen, China
[email protected]
with the sternum. The ribs have stable shape, and map to high intensities in CT data. The rib structures enclose the complete chest and part abdomen. Furthermore they are symmetrical and highly ordered. These rib features can be used for reliable registration while the segmented ribs as reference objects can help segment other structures [3-4].
Abstract—An automatic segmentation method for extraction of human rib structures from chest CT volume data is presented. Segmentation is initiated from the middle coronal slice to attain complete and isolated 12 pairs of ribs with a recursive tracking on coronal slices spreading from the middle coronal slice. At each coronal slice, the lung contours are extracted; candidate rib regions are derived from thresholding, and refined by adding constraints on shape and location with respect to the lung contour and centroids of rib regions of the former coronal slice. Anatomical and radiological prior knowledge has been explored to ignore those rib regions connected to spine for breaking the connection with spine. Appropriate thresholds are chosen so that lung regions can be binarized, the cartilage connecting ribs and sternum are binarized as background to break the connection between the sternum and ribs. Our method is tested on 15 CT data sets. Experiments show that radiologists are satisfied with the extracted rib regions on coronal slices, and only those rib regions connected to spine are discarded (5.5% of all 2D rib regions). The method provides a foundation for further investigation on computer-aided diagnosis of rib fractures.
To the best of our knowledge, rib segmentation has not been paid much attention. In [5-7], the ribs, sternums and spines which connected as one region was segmented. Several methods which used to segment elongated structures were applied to segment the ribs in CT data [8-9]. In [5] a tracking method based on region growing was described, which needs manual seeds to proceed on slices one by one. In [10] the method was based on locally adaptive thresholds and 3D region growing. In [11-12], a centerline tracing method previously developed for the vasculature segmentation was proposed which is initiated with the rib seed points found from the middle coronal slice. In [13-14], a supervised method was proposed to obtain the rib structures with a region growing process, in which the seeds were primitives of rib centerlines labeled from non-rib primitives using a trained classifier. [13] might miss the 11th and 12th pairs of ribs, while [14] could need a long computational time. In [15], a rib cage model constructed from training set was used. In [16], each rib was grown from the seed region which is detected based on the identified spinal centerline.
Keywords- Rib Segmentation; Computed Tomography; Threshold; Tracking
I.
INTRODUCTION
The diagnosis for various kinds of diseases has been becoming efficient due to the progress of medical imaging technology. But meanwhile large amount of data is produced by the three dimensional (3D) imaging modalities, such as CT imaging, which is difficult to be interpreted by radiologists. Hence, post-processing methods by the computer are being developed to solve this problem. [1] gives numerous examples, of which automated rib segmentation is the one we are interested in.
Using region growing based on grayscale is a feasible way. However, it’s difficult to separate ribs from spines and sternum. In this paper, we propose an algorithm in which a recursive tracking process is performed while prior knowledge is employed to get rid of spine and sternum. The rest of this paper is organized as follows. Section II describes each stage of the algorithm. Section III presents experiment results. Section IV is devoted to discussion and conclusions.
Rib segmentation is of great significance to rib cage visualizations and to computer-aided rib abnormalities diagnosis [2]. Human body has 12 pairs of ribs which are connected with the vertebral column at the posterior end, while at the anterior end the upper 10 pairs are connected 978-0-7695-4706-0/12 $26.00 © 2012 IEEE DOI 10.1109/iCBEB.2012.89
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II.
METHOD
Chest CT Scans
Our rib extraction algorithm includes two main steps. First, rib regions are extracted in a coronal slice close to the center of the chest (middle coronal slice). Second, a tracking process is performed from the middle coronal slice in posterior and anterior directions. At each coronal slice, the segmentation consists of finding lung contour, followed by delineating rib regions incorporating radiological and anatomical knowledge. The pipeline of our method is illustrated in Fig. 1, and the detailed steps are described below. The 3D coordinate system follows the following convention: the x axis is from left to right, y from anterior to posterior and z from superior to inferior.
Preprocessing Extraction of lung contour and ROI Segmentation of middle coronal slice Tracking segmentation Rib cage
A. Prepocessing Various imaging protocols adopted by technicians in different medical centers causes difficulties for automatic image analysis. For example, different physicians may employ different slice thickness in the axial space during chest CT scanning. Noise in data from high and low dose scans may have different levels. The high noise in low dose CT data can lead to obscure rib boundaries.
Figure 1. The pipelines of our proposec method.
set as within 20 pixels). Because of noises and partial volume effect, an elliptic rib region may appear in more than one region. Hence, a close operation with a square SE of 2 pixels in side is performed to merge small regions which originally belong to one rib region.
Hence, in the image preprocessing step, we have to deal with these two major issues. The original data is converted into isotropic data and a median filter is selected for noise removal.
Applying all the rules above, we extract all rib regions. And the center points of these regions are saved and denoted as an array p256 (i), with p256 (i) = (xi256, 256, zi256) as the starting points of the following tracking procedure. Due to the extent of the CT scan, 9-11 pairs of rib regions and corresponding centroids are obtained, by which the tracking spreads in both directions.
B. Extraction of Lung Contour The CT intensity value of lung ranges from -500 to -900 HU (Hounsfield Unit). We select a threshold of -500 HU for binarization. A morphological close operation with a square structuring element (SE) of 6 pixels in side is applied to the binarized image to fill gaps within the lung. At last the lung contour which helps locate the rib regions on each coronal slice is obtained by a gradient magnitude filter.
D. Tracking Starting at each centroid point from the above step, tracking process is initiated in both directions, from the center of the chest to the posterior vertebral column (from the 256th to 512th), and to the anterior sternum (from the 256th to 1st).
C. Processing of Middle Coronal Slice In order to start the following tracking segmentation, we first choose the middle coronal slice to process. The data size is in the range of 512×512×400 ~512×512×700 voxels, so we usually choose the 256th coronal slice as the middle coronal slice. This slice intersects with almost all of the ribs, but does not contain any spine or sternum region. And the ribs on this coronal slice are small regions which are similar to ellipses in shape, locating uniformly around the leftmost and rightmost boundaries of the lung (Fig. 2 (b)).
For the 255th coronal slice, the same threshold for bone is employed to binarize the 255th coronal slice. Then, rib regions are searched around the corresponding points (xi256, 255, zi256) within a circular area of a radius of 50 pixels radius in the binarized image. Spreading in this direction, rib regions are constrained to be within 20 pixels of the lung boundary in horizontal direction. After all the rib regions are found, the centroids of ribs are updated as p255 (i) = (xi255, 255, zi255) for processing the 254th coronal slice. This process goes on until the 1st coronal slice is processed. As the cartilage which connects the sternum and ribs have a density below 110 HU, the thresholding process to find ribs will not contain any sternum.
The threshold at 110 HU for bone is employed to binarize the coronal slice, and foreground regions are labeled as light, which are the ROIs (region of interest). Size constrains are employed to eliminate most of the non-rib regions. Ribs have a rather stable size on each coronal slice, which is used to exclude non-rib foregrounds with too large or too small sizes. Operationally, the upper and lower sizes are set as 400 and 20 pixels, respectively. This constraint is employed to all coronal slices. Rib regions are required to be close to the lung contour (horizontal distance is operationally
For the 257th coronal slice, there are a few differences. On the one hand, we obtain rib regions through the similar steps to those of to the 255th coronal slice, only the points to search for ribs are changed to (xi256, 257, zi256). On the other hand, we search rib regions around the contour of the middle
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boundaries and vertically 100 pixels below the most inferior pair of ribs. This extra step functions in 2 scenarios. The first is to prevent missing of rib regions which are between the left and right lung contour when images contain spine regions (marked by yellow ellipses in Fig. 2(d) ), while the second is to find new rib regions ( marked by yellow ellipses in Fig. 3), i.e., the 10th , 11th, and 12th pairs of ribs. As shown in Fig. 3, if the new region located vertically below the most inferior pair of ribs appears in not less than three consecutive slices irrespective of their small sizes, it will be considered as a rib region in the consecutive coronal slices. After all the rib regions are found, the centroids of ribs are updated as p257 (i) = (xi257, 257, zi257) for processing the 258th coronal slice. This process goes on until the 512th coronal slice is processed. As large size foreground regions are discarded which corresponds to foreground regions with spine, the connection with spine is broken. The additional searching around the vertical direction of the most inferior pairs of ribs ensures that 12 pairs of ribs could be segmented and tracked. III.
Figure 2. (a) The 256th coronal slice; (b) the binarized image; (c)the lung contour and the rib regions in the 256th image; (d) the 335th binarized slice in which spine region and rib region between left and right lung boundaries appear.
EXPERIMENT AND RESULT
In this section, results of each processing step in the experiments are presented. The original CT dataset were provided by Shandong Linyi People's Hospital. Among these dataset, half of them contain rib fractures, and the rest are normal scans. In our experiments, 15 chest CT dataset were tested. The original dataset were converted into isotropic data, of which the resolution in each direction ranges from 0.5 mm to 0.8 mm.
Figure 3. Result of the 260th , 261th , 262th, 263th coronal slice (from left to right).
Shown in Fig. 2(c) are the lung contour and the segmentation result of the 256th slice are. It can be seen that soft tissue and miscellaneous bones in the original coronal slice (a) are removed completely and only 21 rib regions (c) are kept.
Figure 4. Non-rib region and missing rib regions: (a) the 169th coronal slice; (b) result of the 169th; (c) the 340th ; (d) the 340th binarized slice in which part rib regions are connecting with spine regions.
Fig. 3 and 4 (b)-(c) show the results of tracking in several representative slices. The rib regions in different coronal slices vary in shape and distribution. In Fig. 4 (b), there is a misjudged rib region which belongs to the clavicle region for its similar shape feature to that of rib regions (marked by the yellow ellipse). As shown in Fig. 4 (c), several rib regions which should originally locate in the yellow ellipse are missing in the 340th coronal slice while in other three coronal slices rib regions are complete. For the 340th coronal slice, the missing rib regions are found by the tracking and removed later by the size constraint for its connecting with the spine regions (marked by yellow ellipse in Fig. 4 (d)). Shown in Fig. 5 is an example of the rib cage constructed from segmentation of 512 images. We can see that ribs are almost complete except a little missing in areas connecting with vertebral column compared with results of manual segmentation.
Figure 5. The 3D display of the rib cage.
radiologists matched well in most coronal slices. Of the regions manually identified, 94.5% is extracted by our method; and of the regions extracted, less than 1% is non-rib region (marked by a yellow ellipse in Fig. 4 (b)).
Quantitative comparative analysis of the rib regions is performed in the following way. Radiologists are asked to count the number of rib regions extracted at coronal slices and visually compare the quality of extracted rib regions. Regions obtained by our method and those identified by the
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IV.
DISCUSSING AND CONCLUSION
[2]
In this paper, we have proposed a rib segmentation method for chest CT volume data. A prior knowledge based tracking segmentation is applied for the complete rib structure. The tracking mechanism and size differences between ribs and spines play a crucial role in separating ribs from spines. Thresholds and parameters contained in the procedure were experimentally determined with several adult chest CT dataset. Segmentation of the complete set of rib structures from a data set of 685 image slice on a 2.4 GHz Dual Processor and 3 GB memory Pentium PC takes about 80 seconds. The rib structure could be used not only to help for locating the internal organs in the thoracic and abdominal area by developing relative coordinate systems, but also to help for the further computer-aided diagnosis of rib fractures.
[3]
[4]
[5]
[6]
By experiments the performance of the segmentation method proposed in this paper is proved to be satisfying for short computation time and almost complete rib visualization. From the quantitative evaluation, we can see that there are 5.5% of rib regions which are missing for the disconnection of ribs from the spine, and parts of clavicle regions are misjudged as rib regions, this part could be eliminated by considering the extension in y direction (as most parts of the clavicle regions are more than 20 pixels away from the lung contour in horizontal direction such that they were not considered foreground regions). The algorithm provides a way to derive 12 pairs of isolated ribs by breaking the connection between ribs and spines without missing much of the ribs (around 5.5% rib regions closest to spines are missing). Our future research will focus on visualization of ribs which could display the information of rib fracture more directly and clearly. This work is also a part of our ongoing CAD system for the detection of rib fracture.
[7]
[8]
[9]
[10]
[11]
[12]
ACKNOWLEDGMENT The authors would greatly thank Jingui Du and Zhenchao Sun (Shandong Linyi People's Hospital) for providing the clinical chest CT datasets. This work was supported by Shenzhen Key Laboratory of Neuro-Psychiatric Modulation.
[13]
[14]
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