Computer-aided system for measuring the mandibular cortical width on panoramic radiographs in osteoporosis diagnosis Agus Zainal Arifin*a, Akira Asanob, Akira Taguchic, Takashi Nakamotoc, Masahiko Ohtsukad, and Keiji Tanimotod a Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi Hiroshima, Hiroshima, 739-8527 JAPAN; b Division of Mathematical and Information Sciences, Faculty of Integrated Arts and Sciences, Hiroshima University, 1-7-1, Kagamiyama, Higashi-Hiroshima, 739-8521, JAPAN; c Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima-shi, 734-8553, JAPAN; d Department of Oral and Maxillofacial Radiology, Division of Medical Intelligence and Informatics, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima-shi, 734-8553, JAPAN
ABSTRACT Osteoporotic fractures are associated with substantial morbidity, increased medical cost and high mortality risk. Several equipments of bone assessment have been developed to identify individuals, especially postmenopausal women, with high risk of osteoporotic fracture; however, a large segment of women with low skeletal bone mineral density (BMD), namely women with high risk of osteoporotic fractures, cannot be identified sufficiently because osteoporosis is asymptomatic. Recent studies have been demonstrating that mandibular inferior cortical width manually measured on panoramic radiographs may be useful for the identification of women with low BMD. Automatic measurement of cortical width may enable us to identify a large number of asymptomatic women with low BMD. The purpose of this study was to develop a computer-aided system for measuring the mandibular cortical width on panoramic radiographs. Initially, oral radiologists determined the region of interest based on the position of mental foramen. Some enhancing image techniques were applied so as to measure the cortical width at the best point. Panoramic radiographs of 100 women who had BMD assessments of the lumbar spine and femoral neck were used to confirm the efficacy of our new system. Cortical width measured with our system was compared with skeletal BMD. There were significant correlation between cortical width measured with our system and skeletal BMD. These correlations were similar with those between cortical width manually measured by the dentist and skeletal BMD. Our results suggest that our new system may be useful for mass screening of osteoporosis. Keywords: computer-aided diagnosis, osteoporosis, mandibular cortex, mental foramen, image thresholding, and panoramic radiograph.
1. INTRODUCTION Osteoporosis is one of the major problems facing women and older people of both sexes1. This disease is called as the silent epidemic, because the osteoporotic process of general skeletons continues for many years with no symptoms until the fractures occur after the minor impact such as a bump, a fall or a slip. It is manifest by loss of bone mass, structural changes in bone, and increased prevalence of fracture2. The fractures occur mainly in the vertebra, radius and proximal femur. Immediate action to identify individuals, especially postmenopausal women, with undetected osteoporosis would be necessary to prevent osteoporotic fractures. Since bone fragility is mainly dependent on bone mass, bone mineral density (BMD) has been measured to predict the fracture risk from osteoporosis. BMD at the lumbar spine and femoral neck are typically assessed using
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Medical Imaging 2005: Image Processing, edited by J. Michael Fitzpatrick, Joseph M. Reinhardt, Proc. of SPIE Vol. 5747 (SPIE, Bellingham, WA, 2005) 1605-7422/05/$15 · doi: 10.1117/12.594458
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dual-energy X-ray absorptiometry (DXA). However, the availability of DXA scanning facilities is still too limited to identify a large segment of postmenopausal women with undetected osteoporosis3-7. Almost of the elderly visit their dentists frequently more than medical professionals for osteoporosis. Since several works have pointed out that mandibular inferior cortical width below the mental foramen manually measured on panoramic radiographs may be useful indicator of skeletal BMD in postmenopausal women3-7, the dentists may identify postmenopausal women with undetected osteoporosis by dental panoramic radiographs on which teeth and jaws can be observed at a time. However, in general dental office, it is not practical to measure cortical width manually on panoramic radiographs for the identification of postmenopausal women with undetected osteoporosis. The automatic measurement of cortical width on panoramic radiographs may enable us to identify a large number of asymptomatic postmenopausal women with low BMD. The purpose of this study was therefore to develop a computer-aided system for measuring the cortical width of the lower border of the mandible below the mental foramen. Furthermore, the cortical width measured with this system was compared with BMD of the lumbar spine and femoral neck to confirm the diagnostic efficacy of this system.
2. MATERIALS Panoramic radiographs of 100 women aged 50 years or older (mean age; 59.6 years, age range; 50-84), with no previous osteoporosis diagnosis, were used in this study. They visited the Hiroshima University Hospital between 1996 and 2001 for BMD assessment. BMD at the lumbar spine (L2-4) and the femoral neck were measured by DXA (DPX-alpha; Lunar Co., Madison, WI, USA). None of the subjects had any metabolic bone disease (hyperparathyroidism, hypoparathyroidism, Paget’s disease, osteomalacia, renal osteodystrophy, or osteogenesis imperfecta), cancers with bone metastasis, significant renal impairment and had medications with affect bone metabolism, such as estrogen. Each radiograph was digitalized with the resolution 600 dpi (Fig. 1).
Figure 1: Panoramic radiograph.
3. CORTICAL WIDTH MEASUREMENT The proposed system is a Computer-Aided Diagnosis (CAD) system for measuring the cortical width of the lower border of the mandible below the mental foramen. The schematic diagram as shown in Fig. 2 includes identifying the area of interest, enhancing the original image, determining the margin of cortex, and selecting an appropriate point at which we can measure the width. 3.1. Identifying mental foramen This identification is very important as it leads to the precision of the width we will measure. As an automatic detection of mental foramen of the mandible is difficult, two oral radiologists assisted to determine the position of the mental foramen on original digitized image. The region around the mental foramen is assigned as the region of interest. Original pictures however, are suffered with the contrast of gray level. Dark color dominates the area around mental foramen that will be manually pointed out by the radiologist. Indeed, we need a suitable technique for stretching the contrast so as to identify the mental foramen more easily. Moreover, the area around this point can be assigned as the area of interest. We need to choose a region of interest, because processing the large size of original image may require a long computation time.
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1. Region of interest determination: a. Contrast stretching algorithm b. Determining mental foramen c. Cropping the area around foramen
2. Image enhancement: a. Thresholding algorithm b. High Pass Filtering c. Morphological open and close operation
3. Cortical margin identification: a. Tracing algorithm b. Pixels compression c. Linear regression for each point
4. Distance measurement: a. Line tangential inferior of the mandible b. Determine perpendicular line c. Width of mandibular cortex
Figure 2: Cortical width measurement algorithm.
3.1.1. Contrast stretching algorithm Since some original images have very low contrast, we attempt to improve the contrast in the images, by stretching the appropriate range of intensity value in the area around mental foramen. This process is considered as a transformation from a give gray level u [0, L] into gray level v [0, L] as shown in Fig. 38.
∈
∈
v=f(u)
γ
β α 0
u a
b
L
Figure 3: Contrast stretching transformation.
It increases the gray level range of the desired area, so that the mental foramen can be detected easily. For this purpose, we used a typical contrast stretching transformation, which can be expressed as
αu if 0 ≤ u < a β(u-a) + f(a) if a ≤ u < b (1) γ(u-b) + f(b) if b ≤ u ≤ L. The slopes α,β, andγare chosen by tuning a and b. If the gray scale intervals where involve the area around mental f(u) = v =
foramen is assumed inside the 10% and 60% percentiles, then a and b can be obtained by finding 10% and 60% from the cumulative distributed function, respectively. Note, that since L is the maximum gray level, f(L) is also L. Furthermore, , , and can be determined as:
αβ γ α=
f (a) ; a
β=
f (b) − f ( a) ; b−a
γ =
L − f (b) . L−b
(2)
3.1.2. Determining area of interest The original panoramic radiograph as shown in Fig. 1 has the resolution of 1744x3158 pixels. Therefore, it needs to select the area of interest for saving the computation time. Furthermore, this area has to involve the lower border of mandibular cortex below the mental foramen we have already obtained. Since we have determined mental foramen on the both side, in this step we obtained two areas of interest as shown as two boxes in Fig. 4.
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Figure 4: Determining area of interest.
3.2. Image enhancement Enhancing the image is needed for distinguishing cortical bone from other objects. As the most difficult problems in discriminating the boundary are the illumination variation and low contrast condition, the boundary of each object is not so sharp to discriminate. As well as, gray level variations will be considered as the variation of particular objects. Indeed, we need to remove this side effect by separating all objects from the outer space. Thresholding, which assigns a pixel to one class if its gray level is greater than a specified threshold and otherwise assigns it to the other class, is the most common method for it. After thresholding step, we have already removed the background, as well as preserved all gray levels considered as objects. Thus, we can apply image enhancement, including high pass filtering and morphological operation to construct the boundary of each object. 3.2.1. Thresholding algorithm Fig. 5 and 6 show the histogram of area of interest shown in Fig. 4 for right cortex and left cortex, respectively. Note that the minimum and maximum values in this 8-bit image actually are 0 and 255 respectively, and so straightforward normalization to the range 0 - 255 produces absolutely no effect. However, we can enhance the picture by ignoring all pixel values outside the 10 % and 90% percentiles, and only applying contrast stretching to those pixels in between. Because pixel gray level under 10% is the variation illumination of the background, while gray levels above 90% are assigned as the teeth and label text attached to every image. In addition, the most left hand side (10%) and right hand side (90%), which have very large number of pixels actually do not need to be recognized as background and foreground, respectively. Therefore, these outliers are simply forced to either 0 or 255 depending upon which side of the range they lie on.
Figure 5: Right cortex.
Figure 6: Left cortex.
We have developed a thresholding algorithm that constructing histogram segmentation which maximizing the interclass variance and minimizing the intraclass variance9. Initially, each gray level is assigned as a different cluster. If we have K gray levels in the image, then we assume that there are K classes, C1, C2, … CK, which gray level Tk is contained in Ck, and it satisfies T1 < T2 < …
