Automatic Classification System for Lumbar Spine X-ray Images

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1 Automatic Classification System for Lumbar Spine X-ray Images Soontharee Koompairojn Kien A. Hua School of Electrical Engineering and Computer Science University of Central Florida Orlando, FL {soonthar, Chutima Bhadrakom Thai Nakarin Hospital Bangna, Bangkok 10260, Thailand Abstract Existing computer-based spinal stenosis diagnosis systems are not fully automatic. Their performance depends on the knowledge and experience of the user. Such a system is typically intended for specialists such as radiologists. We present in this paper a fully automatic system, more suitable for general practitioners for use in screening and initial diagnosis. To evaluate the performance of the proposed techniques, we build a system prototype with two environments one for managing training images and building the classifiers, and the other environment for diagnosis use in practice. Our experimental results, based on an X-ray image database NHANES II available from the National Library of Medicine, indicates that the proposed system is effective for screening purposes. 1. Introduction Lumbar spinal stenosis is the leading preoperative diagnosis for adults older than 65 years who undergo spine surgery. The cost of more than 30,000 Lumbar spinal stenosis surgeries performed in 1994 exceeded 1 billion dollars [6]. The symptoms of spinal stenosis are nerve root pain and or back pain due to spinal canal and disc narrowing and/or thickened facet joints. The best diagnosis of spinal stenosis can obtain from either Magnetic Resonance Imaging (MRI) scan or a Computer Tomography (CT) scans. They are excellent diagnosis tools that provide details about ligament, tendon, bone and tissue. However, the examination with MRI or CT is expensive. They are used to confirm or diagnose for the severe symptoms and/or use before the surgery. X-ray imaging is the fundamental tool to reveal some evidence when the patient first visits. It provides some basic information before performing the advanced imaging. The lateral view of L-spine can also give some abnormal details such as osteophyte, hypertrophy of apophyseal facet joint. In this paper, we propose a classification software system for automatic screening of lumbar spine X-ray images for abnormal spinal stenosis. The system would estimate the width of spinal canal and some other features in order to classify and predict if the patient has a positive spinal stenosis. Our experimental results indicate that the proposed techniques are effective. Such a system is inexpensive, and could be used by internists and general practitioners to examine spinal conditions and perform initial diagnosis in their own clinic. The remainder of this paper is organized as follows. Section 2 provides a review of related works. We introduce our methodology in Section 3. Section 4 describes the system prototyping and we discuss our experimental study in Section 5. Finally, the conclusion and future work are presented in Section Related work Several researches on spine X-ray images have been studied. The Communications Engineering Branch of the Lister Hill National Center for Biomedical Communications, an R&D division of the National Library of Medicine (NLM), developed the Web-based Medical Information Retrieval System (WebMIRS) [9] to provide the internet access to a small set of records and associated X-ray images from a database of spine X-ray images collected by the second National Health and Nutrition Examination Survey (NHANES II) [8]. A Digital Atlas [9] of the cervical and lumbar spine is another software system developed by NLM. It presents the standard reference X-ray images for osteoarthritis, and provides software tools for enhancing X-ray images for manual diagnosis of osteoarthritis.

$[7] utilized the anterior and posterior vertebral height in diagnosis of spinal fractures. Prior studies have evaluated the diseases and spinal conditions in the cervical spine and lumbar spine.$

2 Vertebral Morphometry [10] is a computerized quantifying technique for measuring the vertebrae on lateral radiographs of the spine. Hedhund, et al. [7] utilized the anterior and posterior vertebral height in diagnosis of spinal fractures. Prior studies have evaluated the diseases and spinal conditions in the cervical spine and lumbar spine. Chamarthy et al. [1] introduced image analysis techniques to characterize the disc space narrowing in cervical vertebrae. However, the testing images need to be labeled with the boundary points. Cherukuri et al. [2] presented the anterior osteophyte discrimination technique in lumbar spine X-ray images. Stanley et al. investigated image analysis techniques to recognize the subaxial subluxation in cervical spine X-ray images [11]. None of the aforementioned techniques for diagnosis of spinal pathology is fully automatic. Their effectiveness depends on the knowledge and experience of the user. Such systems are mainly intended for radiologist users. The motivation for our research is to develop a fully automatic solution, more suitable for general practitioners for screening purposes. 3. Methodology Lumbar spine is the lower portion of the spine structure (i.e., lower back) as shown in Figure 1.a. Vertebrae are the bones that make up the lumbar spine. There are five lumbar vertebrae (L1 to L5) and five sacral (lowest area of the back) vertebrae (S1 to S5) [5]. Only S1 is shown in Figure 1.a. Between two vertebrae is the intervertebral disc and the spinal canal as illustrated in Figure 1.b. The intervertebral disc level consisting of the spinal canal and the intervertebral disc between L1 and L2 is referred to as L1-L2 level. Similarly, we have four additional levels L2-L3, L3-L4, L4-L5, and L5-S1 as shown in Figure 1.a. As the discs becomes less spongy and less fluid filled with age, spinal stenosis is caused by narrowing of the lumbar spinal canals, usually at levels L3-L4, L4-L5, and L5-S1. The details of two adjacent vertebrae are illustrated in Figure 1.b. It shows that the spinal canal contains spinal cord and nerve. The front of spinal canal is the posterior border of the vertebrae and intervertebral disc space. The back of spinal canal is the inferior and superior facet joints, called posterior apophyseal joints. We observe that the front of spinal canal is flat while the back curves along the facet joint. The drawback of X-ray images is that they show only the bone structure; the soft tissue component such as disc, anterior and posterior spinal longitudinal ligament, and fibrous tissue surrounded facet joint are not demonstrated. Therefore, we can investigate the spinal stenosis only from the following spinal conditions: The narrowing of disc space from degenerative disc or disc protrusion, Posterior osteophytes, Spondylolisthesis, Apophyseal arthropathy (the degeneration of facet joint causes the osseous exostosis.) (a) (b) Figure 1. (a) Lumbar spine structure. (b) Spinal canal and disc space between two adjacent vertebrae. Since patients with similar vertebrae sizes generally have similar spinal canal sizes. This characteristic allows us to develop a spinal stenosis classifier using a set of reference X-ray images for spinal stenosis as the training images. This approach requires a set of visual features which effectively characterize the various spinal conditions in X-ray images Classifiers Construction The classifier is constructed in three steps: (1) Examine the vertebrae, (2) Extract feature, and (3) Build the classifier. We discuss these steps in details in the following subsections Examining the Vertebrae This step processes the X-ray image to prepare for feature extraction. For each training X-ray image, the radiologist marked 11 points along the boundary for each lumbar vertebra as showed in Figure 2. These points are at the top and bottom of anterior (points 3 and 6) and posterior (points 1 and 4) sides, at the mid point of inferior (point 2) and superior endplate (point 5), the anterior midpoint (point 7), the anterior osteophyte (points 8 and 9), and the posterior osteophyte (points 10 and 11). We note that points 1 9 are the same as proposed in [9]. We introduce the additional two more points for our purposes. In

3 addition, we use two more points as illustrated in Figure 2. It shows the two parallel lines intersecting the posterior border of the spinal canal at these two points (points 12 and 13). Figure 2. The illustration of 11-point Vertebra and two additional points. Figure 3. The illustration of extracted features Extracting Features for Training Our features are extracted to characterize the following four distinct spinal conditions: posterior osteophyte, posterior apophyseal arthropathy, disc space narrowing, and spondylolisthesis. Diagnosis of lumbar stenosis is done by examining these spinal conditions through feature analysis. Our feature extraction is adapted from the vertebral morphology technique proposed in [10] using the 13 points described in Section For each of the five intervertebral disc levels, we perform feature extraction as follows: For each vertebra, the following features are extracted. Anterior vertebral height: this is the distance from point 3 to point 6. Mid vertebral height: this is the distance from point 2 to point 5. Posterior vertebral height: this is the distance from point 1 to point 4. These features are shown as line A, B, and C in Figure 3 respectively. For the intervertebral disc, the following features are extracted. Anterior height of intervertebral disc space: this is the distance from point 6 of the upper vertebra to point 3 of the lower vertebra. Mid height of intervertebral disc space: this is the distance from point 5 of the upper vertebra to point 2 of the lower vertebra. Postrior height of intervertebral disc space: this is the distance from point 4 of the upper vertebra to point 1 of the lower vertebra. Upper anteroposterior (A-P) width of usual spinal canal: this is the distance from point 4 of the upper vertebra to point 12 at the posterior boundary of the spinal canal. Lower anteroposterior (A-P) width of usual spinal canal: this is the distance from point 1 of the lower vertebra to point 13 at the posterior boundary of the spinal canal. Upper anteroposterior (A-P) width of unusual spinal canal: this is the distance from posterior osteophyte point 11 of the upper vertebra to point 12 at the posterior boundary of the spinal canal. Lower anteroposterior (A-P) width of unusual spinal canal: this is the distance from posterior osteophyte point 10 of the upper vertebra to point 13 at the posterior boundary of the spinal canal. These features are shown as line D, E, F, G, H, I and J in Figure 3 respectively Building Classifiers After extracting the features for the training X-ray images, this information is used to build a classifier for each spinal condition. We use a Bayesian framework to learn the positive or negative values of each spinal condition. The input to the system is a set of extracted features of the training images examined by the radiologist. Let x 1,,x n be the extracted features using for each spinal condition b. The posterior probability for each positive and negative of the spinal conditions can be calculated as follows px (,..., x bpb ) ( ) 1 n pbx (,..., x) = 1 n px (,..., x) 1 n

3.2. Automatic Diagnosis Environment Using the classifiers, automatic diagnosis is done in two steps: (1) extract features, and (2) predicting spinal conditions.

Extract Features for Automatic Diagnosis We applied the image segmentation technique Active Appearance Modeling (AAM) [12] to automatically label the boundary points of vertebrae.

4 3.2. Automatic Diagnosis Environment Using the classifiers, automatic diagnosis is done in two steps: (1) extract features, and (2) predicting spinal conditions. We discuss these steps in details in the following subsections Extract Features for Automatic Diagnosis We applied the image segmentation technique Active Appearance Modeling (AAM) [12] to automatically label the boundary points of vertebrae. AAM creates a deformable model, and uses it to estimate the boundary points of the X-ray image under diagnosis. Figure 4 shows the result of AAM with lines drawn to connect the points to close the boundaries of the vertebrae. We note that our framework is generic; and other image segmentation techniques such as Active Contour Segmentation [13], Active Shape Modeling [3], and Shape Particle Filtering [4] can also be used. After the points have been labeled using AAM, we can extract features as described in Section System Prototyping To assess the effectiveness of the proposed methodology, we build a system prototype to facilitate experimental studies System Architecture Our Spinal Stenosis Diagnosis (SSD) System consists of two parts as presented in Figure 5 the classifier building environment on the right hand side, and the end-user automatic stenosis diagnosis environment on th left hand side. The training environment provides the user interface to allow the user to extract features from the training X-ray images, and subsequently apply them to the training and learning process to build the classifiers, one for each of the four stenosis conditions. In production time, a physician can feed the X-ray image of a new case to the user interface in the left hand side. Internally, features of this image are automatically extracted and analyzed, and the final predicted diagnosis is returned to the physician. Figure 5. The system architecture of proposed system. Figure 4. The segmentation result showing five lumbar vertebrae Predicting spinal conditions With the features extracted, the classifiers can be used to diagnose the case represented by the X-ray image as follows. With the posterior probability from Section 3.1.3, the positive or negative result for each spinal condition can be determined by b * = arg max p( b x1,..., xn) We recall from the beginning of Section 3 that the spinal stenosis arises from four spinal conditions (posterior osteophyte, posterior apophyseal arthropathy, disc space narrowing, spondylolisthesis). The predicted results from those conditions can be used to evaluate the spinal stenosis User interface for Building Classifier A screen shot of the user interface for managing test images and building classifiers is presented in Figure 6. It has two parts. The hierarchy on the right shows the data files of the training images. They are organized according to intervertebral disc levels. Within each level, the image files are stored according to their spinal condition. For each condition, the system accepts training images for both positive and negative cases. The user can click on a training image file to view the X-ray image and browse its information, as shown on the left of Figure 6. Through this interface, the user can submit training images and build the lumbar stenosis classifiers User interface for Automatic Diagnosis A screen shot of the production environment is given in Figure 7. The user can open the new lumbar

X-ray image and use the system to automatically generate the labeling points. The user can then click on the analysis button to analyze the spinal conditions and diagnose lumbar spinal stenosis.

The user interface for managing training images and building lumbar spine classifiers. Figure 7. The end-user interface for submitting X-ray images for analysis of spinal conditions. 5.

5 X-ray image and use the system to automatically generate the labeling points. The user can then click on the analysis button to analyze the spinal conditions and diagnose lumbar spinal stenosis. A final diagnosis report for a specific case is shown in Figure 7. It presents the corresponding X-ray image on the left, and a summary of the diagnosis results in the middle of the screen. Figure 6. The user interface for managing training images and building lumbar spine classifiers. Figure 7. The end-user interface for submitting X-ray images for analysis of spinal conditions. 5. Experimental Studies To evaluate the performance of the proposed automatic lumbar stenosis diagnosis system, we performed experiments to assess the effectiveness of the stenosis classifier using the lateral view of 86 lumbar spine X-ray images from the NHANES II database [9], available from National Library of Medicine. We did 20 experiments. For each experiment, we selected 70 images as the training set to build the classifiers, and the remaining 16 images are used for classifier testing. The results, reported in this section, are based on the average over the 20 experiments. The NHANES II database includes the label points 1-9 for each lumbar X-ray image. To build the classifier for the posterior osteophytes, the radiologist labeled two more points (points 10 and 11) for each image as illustrated in Figure 2. To provide the ground truth for training, the radiologist also evaluated the spinal conditions (posterior osteophyte, posterior apophyseal arthropathy, disc space narrowing, spondylolisthesis, spinal stenosis) for each image. Since the spinal canals of L5-S1 were not clear for many images, only the spinal canals at levels L2-L3, L3-L4 and L4-5 were investigated in this study. The positive results of this manual evaluation are presented in Table 1. Spinal conditions L2-L3 L3-L4 L4-L5 Total Posterior osteophyte Posterior apophyseal arthropathy Disc space narrowing Spondylolisthesis Spinal stenosis Table 1. The experimental data set. To construct the 12 classifiers for each of the 20 experiments, the features of the training images were extracted to build the classifiers using the diagnosis information provided by the radiologist. For each spinal condition (e.g., disc space narrowing), we trained and built a classifier for each of the three intervertebral disc levels L2-L3, L3-L4, and L4-L5. Thus, there are 12 (4 3) classifiers in total. For some spinal conditions, such as posterior osteophyte and spondylolisthesis, which have a few positive cases, we considered multiple intervertebral disc levels together in the training. The training results are presented in Table 2. It reports the average percentage of correct prediction of each classifier based on the training data set. To evaluate the effectiveness of the classifiers, we apply them to the test images. We first used the original X-ray images with resolution. The AAM segmentation technique, however, did not perform well, and we had to reduce the resolution to With this lower-resolution setting, the average point-to-point error (i.e., the Euclidean distance) between each labeling point, obtained by the AAM technique, and its correct location in the same image is 9.74 pixels. These errors range from 3.49 pixels to pixels with a standard deviation of After labeling the points, we ran the system to automatically predict the positive or negative spinal

6 condition for each of the 16 testing images. The average percentages of correct prediction of the 12 classifiers for the test cases are shown in Table 3. Overall, the accuracy ranges from about 70% to 80%, which is effective for screening purposes. Table 4 shows the same average measures if the 12 feature points could be labeled correctly. The performance, under this hypothetical situation, improves to about 85% on average. This observation indicates the performance potential of the proposed system with better segmentation methods. Spinal conditions L2-L3 L3-L4 L4-L5 Average Posterior osteophyte Posterior apophyseal arthropathy Disc space narrowing Spondylolisthesis Spinal stenosis Table 2. The average percentage of correct prediction of training experimental data set. Spinal conditions L2-L3 L3-L4 L4-L5 Average Posterior osteophyte Posterior apophyseal arthropathy Disc space narrowing Spondylolisthesis Spinal stenosis Table 3. The average percentage of correct prediction result of experimental data set. Spinal conditions L2-L3 L3-L4 L4-L5 Average Posterior osteophyte Posterior apophyseal arthropathy Disc space narrowing Spondylolisthesis Spinal stenosis Table 4. The average percentage of correct prediction of optimal result of experimental data set. 6. Conclusions and future work In this paper, we described an automatic system for spinal stenosis diagnosis. Since its performance does not depend on the knowledge and experience of the user, such a system is more suitable for general practitioners in performing screening and initial diagnosis. To assess the performance of the proposed techniques, we built a prototype, and performed experiments using an X-ray image database from the National Library of Medicine. The experimental results show accuracy ranging from 75% to 80%, which are acceptable for screening purposes. Our study also indicated that the proposed methodology can improve to 85% on average, with better image segmentation techniques. Future research may investigate better segmentation techniques for X-ray images. Additional visual features such as those available from anteroposterior spine X-ray images can be explored to enhance the classifier effectiveness. The overall performance can also be improved by considering additional spinal conditions. 7. References [1] P. Chamarthy, R.J. Stanley, G. Cizek, R. Long, S. Antani, G. Thoma, Image analysis techniques for characterizing disc disc space narrowing in cervical vertebrae interfaces, Computerized Medical Imaging and Graphics, 2004, Vol. 28, pp [2] M. Cherukuri, R.J. Stanley, R. Long, S. Antani, G. Thoma, Anterior osteophyte discrimination in lumbar vertebrae using size-invariant features, Computerized Medical Imaging and Graphics, 2004, 28,pp [3] T.F. Cootes, G.J. Edwards, and C.J. Taylor, Active appearance model, In Proc. 5th European Conference on Computer Vision, Freiburg, Germany, [4] M. de Bruijne and M. Nielsen, Image segmentation by shape particle filtering, in International Conference on Pattern Recognition, J. Kittler, M. Petrou, and M. Nixon, eds., pp. III: [5] S.G.Eidelson, Lumbar spine, [6] M.B. Furman,MD and K.M.Puttlitz,MD, Spinal Stenosis and Neurogenic Claudication, EMedicine. [7] L.R.Hedlund, J.C.Gallagher, Vertebral morphometry in diagnosis of spinal fractures, Bone Miner 1988,pp [8] L.R. Long, S. Antani, D.J. Lee, D.M Krainak, and G.R. Thoma, Biomedical Information from a National Collection of Spine X-rays: Film to Content-based Retrieval, Proceedings of SPIE Storage and Retrieval for Still Image and Video Database 2003 [9] L.R. Long,, S. Antani, and G.R. Thoma, Image informatics at a National Research Center, Computerized Medical Imaging and Graphics 29, 2005,pp [10] P.H.F. Nicholson, M.J. Haddaway, M.W.J. Davie and S.F. Evans, A computerized technique for vertebrae morphometry, Physiol. Meas, Volume 14, pp [11] R.J.Stanley, S.Seetharaman, L.R.Long, S.antani, G.Thoma, and E.Downey, Image Analysis Techniques for the Automated Evaluation of Subaxial Subluxation in Cervical Spine X-ray Images, Proceedings of the 14 IEEE Symposium on Computer-Based Medical Systems 2004, [12] M.B. Stegmann, Active appearance Model, theory extensions and cases, Master Thesis, Technical University of Denmark, [13] H.Tagare, Deformable 2-D template matching using orthogonal curves, IEEE Transaction Medical Imaging 1997; 16(1), pp

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