XXIII Congresso Brasileiro em Engenharia Biomédica – XXIII CBEB
Detection of Vertebral Compression Fractures in Lateral Lumbar X-ray Images E. A. Ribeiro*, M . H. Nogueira-Barbosa*, R. M. Rangayyan**, P. M. Azevedo-Marques*
*School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil **Department of Electrical and Computer Engineering, Schulich School of Engineering. University of Calgary, Calgary, AB, Canada
[email protected] performed manually, the usability of such methods is not practical on a large scale. Genant et al. [2] proposed a semiquantitative method for the evaluation of vertebral fractures related to osteoporosis, which combines numerical classification based on visual inspection. Analysis of spinal and vertebral deformities could assist in the diagnostic decision-making process and in guiding therapeutic procedures [3-6]. In such a scenario, digital image processing is desired to facilitate the development of automated or semiautomated procedures that could lead to more efficient and accurate analysis of vertebral deformities. A few recent studies have proposed computer-aided methods for the detection and analysis of vertebral bodies in radiographic images [7–13]. The objective of this work is computer-aided diagnosis (CAD) of vertebral fracture. We propose digital image processing and pattern recognition techniques [14] for the extraction of measures of vertebral bodies in lateral X-ray images of the lumbar spine. The proposed techniques are based on a semiautomatic segmentation procedure using Gabor filters [17], detection of landmark points in the inferior and superior plateaus of each vertebral body, and automatic extraction of measures of the height of each vertebral body in order to classify each vertebra as normal or deformed (compressed).
Abstract: We present a method for extraction and analysis of vertebral bodies for the computer-aided diagnosis (CAD) of compression fractures. Images of 160 vertebral bodies were extracted from lateral lumbar spinal X-ray images. A total of 80 normal vertebrae bodies were extracted from exams of 20 normal individuals. A total of 80 fractured vertebrae bodies were extracted from exams of 34 patients diagnosed as having vertebral fractures. Gabor filters and an artificial neural network were applied to extract the superior and the inferior plateaus of each vertebral body. Next, the anterior, posterior, and central heights of the vertebral bodies were derived automatically. The measured heights were analyzed using the semiquantitative grading proposed by Genant et al. for the detection and classification of vertebral compression fracture. The results of CAD were compared with the results of visual and manual analysis. The results demonstrate a high correlation between the measures made semi automatically and the visual inspection. A sensitivity of 0.78 and a specificity of 0.95 were achieved via the proposed CAD, indicating a promising method for morphometric analysis of vertebrae. The results are expected to be useful in the analysis of vertebral deformities and fractures.
Key Words: Vertebral Morphometry, CAD, Image Processing Introduction Vertebral fractures are importante indicators of severe osteoporosis and are considered as an important risk factor to predict future fractures in patients with osteoporosis. Spinal radiography continues to have a substantial role in the diagnosis and follow-up of vertebral fractures [1, 2]. The primary evaluation of vertebral deformity in individuals with osteoporosis is typically performed via radiographic studies, including morphometric assessment of vertebral bodies [3–7]. Both semiquantitative and quantitative methods have been used to achieve objective and reproducible definition of the associated findings [2]. In current clinical practice and in epidemiologic studies of osteoporosis, most of the procedures for quantitative morphometric analysis of vertebral bodies are performed manually, in a manner that is labor-intensive and subject to significant interobserver and intraobserver variability. Furthermore, due to the expensive work involved with quantitative evaluation
Material and Methods Lateral lumbar spinal X-ray images were obtained from imaging files at the Clinical Hospital of the Faculty of Medicine, University of São Paulo, at Ribeirão Preto, SP, Brazil. Images of 160 vertebral bodies were extracted from lateral lumbar spinal X-ray images. A total of 80 normal vertebrae bodies were extracted from exams of 20 normal individuals. A total of 80 fractured vertebrae bodies were extracted from exams of 34 patients diagnosed as having vertebral fractures. The radiographic film images were digitized using a Vidar DiagnosticPro scanner with the spatial resolution of 84 µm and the gray scale represented by 8 bits/pixel. The typical size of the digitized images is about 3000×4000 pixels. In the present study, interest is focused on the four lumbar vertebrae from L1 to L4.
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XXIII Congresso Brasileiro em Engenharia Biomédica – XXIII CBEB For each image, a radiologist (MHNB), with 15 years of experience in spinal and musculoskeletal radiology, manually and independently delineated the vertebral plateaus of L1, L2, L3, and L4 using a software tool for image display and mark-up. Each original image was filtered with a bank of 180 Gabor filters evenly spaced over the range of -90 to 90 degrees. The angle of the Gabor filter with the highest response at each pixel was used to derive a measure of the strength of orientation or alignment. In order to limit the spatial extent of the image data and the derived features used in further analysis, a semiautomated procedure was applied to the original image. The user was required to provide as input one point superior or inferior to each of the vertebral bodies L1 to L4 (five points in total). A semiautomated pipeline based on a neural network utilizing the logistic sigmoid function was trained using the leave-one-out method for image segmentation based on pixel intensity values from the original image, the result of manual delineation of the vertebral plateaus, the Gabor magnitude response, and the strength of alignment [17]. In the segmentation process, fifty eight vertebrae bodies (36%) were not detected. A total of 102 vertebrae bodies were detected and used in further analysis, of which 64 vertebrae bodies are normal and 38 are vertebrae previously diagnosed with fracture via visual analysis by the expert radiologist. Figure 1C shows an example of semiautomatically segmented plateaus from the image presented in Figure 1A. Figure 1B shows the manually drawn plateaus for L1 to L4. Figure 1C is essentially composed of eight lines, representing the plateaus of L1, L2, L3, and L4. A skeletonization [14-16] method was applied after a morphological opening operation to reduce the representation of the detected set of points related to each plateau to a line of 1 pixel width. The skeletonized image allows measurement of the height of each vertebral body, but lacks an axial reference line related to the orientation of the vertebrae. Therefore, a method to derive the convex hull followed by another skeletonization process was applied to define the axis of reference for the vertebrae. The convex hull is defined as the smallest convex polygon that encompasses a set of points [16]. Figure 2A shows the result of the operation for obtaining the convex hull after filling the interior of the polygon, obtained from the points generated in the previous skeletonization step of the vertebral plateaus. To obtain a reference line for measurement, a skeletonization operation was performed on the convex hull, followed by an angle thresholding filter to eliminate spurs (see Figures 2B and 2C). To obtain measures of the heights of the vertebrae, the axial reference line shown in Figure 2C was iteratively shifted to the right and left (anterior and posterior of the patient) by a fixed distance of 20 pixels, established empirically. The stopping criterion used was the absence of intersection with the corresponding vertebral plateaus. The points of intersection were
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identified for each shifted axial reference line with the superior and inferior plateaus of each vertebra, providing the height of the vertebral body at different positions. Figure 3A shows an example of the measurement of the heights for a normal vertebral body and Figure 3B the measurement for a non-normal vertebral body. For the present study, the left-most measure was used as the anterior height, the measure along the main axial reference line was used as the central height, and the right-most measure was taken to represent the posterior height for each vertebral body. Height measures obtained as above were used to verify agreement with the results of visual analysis of the vertebrae. According to the general practice of identification of vertebral fracture, a difference of more than 20% between the three measures of vertebral height is indicative of fracture, considering the highest value as the reference. Furthermore, grading of vertebral fracture was performed using the methods proposed by Genant et al. [2] using manual measurements, made by one of the authors (EAR) using the images of plateaus manually segmented by the radiologist (MHNB) as well as the results of image processing as above. Genant et al. [2] proposed a semiquantitative method for grading vertebrae bodies. The method is based in giving a score, from zero to three, for fracture severity based on the difference between the maximum and minimum measurement of the anterior (AH), central (CH) and posterior (PH) heights, as described in Table 1.
Table 1: Semi quantitave grading for vertebral fractures
Score 0 1 2 3
Percentage difference < 20% 20-25% 25-40% >40%
Results Figure 1 shows examples of images in the process of detection of vertebral plateaus. Figure 1A is the original image, Figure 1B shows the vertebral plateaus manually delineated by an expert radiologist, and Figure 1C displays the results of semiautomatic segmentation of vertebral plateaus.
1A
1B1B
1C 1C
3A 3B
XXIII Congresso Brasileiro em Engenharia Biomédica – XXIII CBEB
Figure 1: Figure 1A is the original image, Figure 1B represents the original image manually delineated by the expert radiologist, and Figure 1C represents the semiautomatically segmented image.
Table 2: Pearson correlation coefficients between the measures of vertebral height obtained semiautomatically by the process proposed in the present paper and the manual measures. AH = anterior height, CH = central height, and PH = posterior height of 102 vertebral bodies, of which 64 vertebrae bodies are normal and 38 are vertebrae previously diagnosed with fracture. AH 0.996
CH 0.994
PH 0.942
AVERAGE 0.994
0.991
0.979
0.818
0.979
0.993
0.998
0.992
0.998
1D
Abnorm al
Norma l
All
Figure 2 shows the process of extraction of the axis of reference for measurement of the heights of vertebral bodies. Figure 3 shows the lines derived automatically to obtain several measures of height for a sample of normal and abnormal vertebrae bodies.
Table 2 shows the Pearson correlation coefficients between the semiautomatically extracted measures of vertebral height and the manual measurements. The high values of the correlation coefficients indicate a very good agreement between the two results.
2D
Table 3 shows the confusion matrix of the results of classification of 102 vertebral bodies as normal or abnormal (fractured). A vertebra was classified as being fractured if the three measures of height differed by more than 20%. Based on the results shown in Table 2, the sensitivity of the proposed method is 0.78 and the specificity is 0.95.
3D
4D
Figure 2: Figure 2A represents the convex hull of Figure 1C. Figure 2B represents the skeleton 2D derived from the result in Figure 2A. Figure 2C is the result after removing the spurs in Figure 2B. Figure 2D is composed by superimposing Figures 1B and 2C, and represents the axis used as the reference in measuring the height of each vertebral body.
Figure 3: Example of measures of height obtained for a normal vertebral body, Figure 3A, and obtained from an abnormal vertebral body, Figure 3B.
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Table 3: Results of semiautomatic diagnosis of vertebral fracture. The reference is the diagnosis made by the expert radiologist based on visual examination of the X-ray images. Visual CAD Normal
Normal
Abnormal
54
10
Abnormal
3
35
Table 4 shows the confusion matrix of the results of grading of vertebral fractures based on the methods given by Genant et al. [2]. The rows of the table show the evaluation made manually. The columns of the table show the results of CAD via semiautomatic evaluation of the vertebral bodies. The values along the main diagonal of Table 3 correspond to correct classification (grading) by the proposed CAD method. It is evident that, in general, the results obtained by the CAD method are in good agreement with the diagnosis made by the expert radiologist. The majority of the errors are related to differences in the grades of 0 and 1, which is also identified as a limitation by Genant et al. in their work [2].
XXIII Congresso Brasileiro em Engenharia Biomédica – XXIII CBEB
Table 4: Results of computer-aided grading of vertebral fracture using the method proposed by Genant et al. The results of manual measures are shown in the rows and the results of the proposed CAD methods are shown in the columns. Manual CAD 0
0
1
2
3
54
0
0
0
1
10
25
2
0
2
0
1
3
1
3
0
0
0
6
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Conclusion The proposed CAD methodology has limitations in the steps of semiautomated segmentation and identification of vertebral plateaus, resulting in the loss of some vertebral bodies for the subsequent steps of height measurement and classification. Further work is in progress to achieve better results in both the segmentation of vertebral bodies and the definition of vertebral fracture. The proposed methods need to be evaluated with a larger dataset. Notwithstanding the limitations mentioned above, the initial results of the present work are promising for quantitative analysis and CAD of vertebral fracture. Such methods have great potential of application in multicenter epidemiological studies in which interobserver differences in assessment can be a major limiting factor.
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Knowledgment This work was partially supported by the National Council for Scientific and Technological Development (CNPq) and by the São Paulo Research Foundation (FAPESP) – Brazil.
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