EARLY OSTEOPOROSIS DETECTION SOFTWARE SYSTEM BY CLAVICULAR CORTEX THICKNESS MEASUREMENT

COMPUTERIZED OSTEOPOROSIS DETECTION BY CLAVICULAR CORTEX THICKNESS MEASUREMENT

J.T. Pramudito*, R.G. Wachjudi**, A.P. Widita*, T.R. Mengko*, S. Soegijoko, F.I. Muchtadi****Scool of Electrical Engineering & Informatics, Institut Teknologi Bandung **Department of Internal Medicine, Hasan Sadikin Hospital, Bandung

*Faculty of Industrial Technology, Institut Teknologi BandungAbstract: Osteoporosis could infect every person, man and woman, without considering their age. It has caused a lot of very uncomfortable and dangerous effects, such as pain, body impairment, and even death. Therefore, preventive action against osteoporosis is very important. Osteoporosis diagnosis method which becomes the gold standard until this time is Dual Energy X-Ray Absorptiometry (DEXA), with the use of T-Score. Unfortunately, DEXA is very expensive and rarely found in remote areas hospitals in Indonesia. This research project develops a software system which could implement image enhancement, image noise removal operation, edge detection, image rotation, and clavicular cortex thickness measurement. The input is thorax x-ray digital image and the output is clavicular cortex thickness (in millimeter). Using thorax x-ray image as input, this software system provides cheaper osteoporosis diagnosis tools and suitable to be used as osteoporosis screening method which could detect osteoporosis earlier. Experiment is performed to 46 digital thorax x-ray image. Compared with the gold standard (DEXA), this system shows 88.89% sensitivity, 90% specificity, 96.97% positive predictive value, 69.23% negative predictive value, and 89.13% accuracy.

IntroductionOsteoporosis is a disease of the skeletons characterized by low bone mass density and micro architectural deterioration of bone tissue, leading to enhanced bone fragility and a consequent increase in fracture risk [1].

According to Indonesian Statistic National Bureau in 2001, about 15 millions people in Indonesia have been infected with osteoporosis and among them, 750.000 people suffered spine and hip fracture every year [2].

Until a healthy person is around 40, the process of breaking down and building up bone by cells called osteoclasts and osteoblasts is a nearly perfectly coupled system, with one phase stimulating the other. As a person ages, or in the presence of certain conditions, this system breaks down and the two processes become out of sync. The reasons why this occurs during aging are not clear. Some individuals have a very high turnover rate of bone; some have a very gradual turnover, but the breakdown of bone eventually overtakes the build-up [3].Histopatologically, osteoporosis is characterized by the decreasing of bone cortex thickness and the decreasing of bone trabecular quantity and/or quality.

Figure 1: Inside the brittle bone

The gold standard for osteoporosis diagnosis until this time is Dual Energy X-Ray Absorptiometry (DEXA). DEXA is used to measure the bone mineral density (BMD) of the patients. Measurements of BMD are given as mg/cm2, which is the average concentration of bone mineral in the areas that are scanned with the imaging tests. Using DEXA, BMD is categorized by a T-score in reference to young normal BMD.To calculate a patients T-score, the patients measured BMD is subtracted with BMD reference range of women in their thirties (YN = young normal). Around age 35, bone mass is usually at its peak and fracture risk is at its lowest. The result is then divided by the standard deviation (SD).

T-Score = (BMD YN) / SD (1)

Table 1: WHO Criteria for Osteoporosis in WomenNormalT-Score > -1.0

Low Bone Mass (Osteopenia)T-Score is -1.0 to -2.5

OsteoporosisT-Score < -2.5

Unfortunately, DEXA is very expensive and rarely found in remote areas hospitals in Indonesia, therefore DEXA is not suitable to be used as early osteoporosis detection method.

While the cost of fractures of hip, spine, and forearm in terms of discomfort and functional loss, as well as depression and other psychological problems is high, earlier diagnosis and treatment would alleviate some of this. Anything that would improve the earlier diagnosing without increasing the cost would be helpful.

Since the thorax x-ray is one of the most common examinations in medical practice and costs cheaper, it would make an ideal diagnostic tool if, at no additional cost, it could be utilized in the diagnosis of osteoporosis.

The purpose of this research is to develop an image processing algorithm which can detect clavicular cortex edges, measure the thickness, and then implement the algorithm through a software system. The algorithm is able to perform thorax x-ray digital image enhancement operation, image noise removal operation, edge detection, image rotation, and clavicular cortex measurement. The software system hopefully could be used as an alternative method for early osteoporosis detection procedure, which is cheaper and easier to use.

Figure 2: A simplified block diagram of the system

First, the patients thorax x-ray image is acquired. And then the analog image is digitalized using image scanner, so that the image can be processed in a PC (personal computer) where the software system developed in this research is already installed. The image becomes the input of the software system, and the output of the system is clavicular cortex thickness measurement results in millimeters. This measurement result is then used by medical doctors to detect osteoporosis, based on hypothesis that thickness measurement result less than 3 mm is considered having osteoporosis problem, while measurement result more than and equal to 3 mm is considered normal [2,4].

Materials and MethodsIn this section, we describe the image processing methods that is developed and used in this research and also the development of the software system.

Image processing algorithm developed in this research is designed using MATLAB. A general block diagram which describes the system is given below:

Figure 3: A general block diagram of the system

Thorax x-ray image is acquired from 46 patients, with equal standardization procedure for each image acquisition. The analog image is then digitalized using CanoScan 9900F Canon Scanner. The digital image is then saved in Windows Bitmap (*.bmp) file type, grayscale (8 bit intensity), 800 dpi resolution, and in 32002400 pixel size.

The image is then used as input for the software system. In the next section, we describe methods being used in the system as seen in the block diagram in figure 3.

1. Image Pre-Processing Operation

The purpose of this operation is to adjust the input image so that the image could be processed in the next operation. It includes image cropping, image enhancement, and image noise removal.

1.1 Image Cropping

The input image needs to be cropped first because we dont need other parts of the image. We only need the area in the clavicular bone, located between the projected medial margin of the scapula and the lateral margin of the adjacent rib [2, 4]. Image cropping is also useful for saving computation time by eliminating unnecessary areas.

(a)(b)

Figure 4: (a) Input Image (b) Cropped Image

1.2 Image Histogram Modification

Histogram modification is one kind of image enhancement technique. The purpose of it is to reduce the effect of low contrast level of the image and to normalize the image intensity level.

The method for histogram modification that is used in this research is contrast stretching, which stretch the distribution of grayscale intensity value in histogram from 0 to 255.

(a)(b)

Figure 5: (a) Image Histogram before Contrast Stretching (b) Image Histogram after Contrast Stretching

1.3 Image Noise Removal

The image is containing noise that comes from the x-ray film, a sequence of vertical lines with the same width and period. These lines are removed by using the smoothing effect of 1-dimensional Gaussian Function Filtering horizontally.

(2)G(x) = Gaussian Function;

= Standard Deviation

(a)(b)(c)

Figure 6: (a) Cropped Image (b) Image after Contrast Stretching (c) Image after Gaussian Filtering

2. Region of Interest (ROI) Segmentation

The purpose of ROI segmentation operation is to detect the clavicular cortex edges and separate them from other part of the image. This process is needed before we continue to the next operation (feature extraction).

ROI segmentation is done with finding the clavicular cortex edges, then rotating the image to a certain degree so that the cortex clavicular position is standardized and the measuring result could be more accurate and precise, and finally linking the edges which were still separated from each other.

2.1 Clavicular Cortex Edge Detection

The edge detection algorithm is performed like this:

Creating vertical gradient image

The output image from pre-processing operation is then filtered with 1-dimensional Gaussian Function differential vertically.

(3)G(y) = Gaussian Function differential;

= Standard Deviation

Detecting the position of the lowest image intensity of the vertical gradient image for each column. The result is become the upper edge of clavicular cortex.

Detecting the position of the highest image intensity of the vertical gradient image for each column. The result is become the lower edge of clavicular cortex.

The result form this algorithm is still an early edge detection, because the edges is still separated from each other.

(a) (b)

Figure 7: (a) Image before edge detection (b) Image after edge detection2.2 Image Rotation

The purpose of image rotation is to standardize the position of the clavicular cortex and make the measurement result more accurate and precise. Clavicular cortex edges that we get from earlier operation, is creating an angle ( degree) compared to horizontal reference line.

First step to do is to find a representation linear line of the clavicular cortex edges. This linear line is created from linear regression operation from upper clavicular cortex edge point coordinates. Then we compare the linear line with the horizontal reference line to get the rotation angle ()

Figure 8: Measurement of rotation angle

The image is then rotated for using following transformation equation:

(4)2.3 Edge Linking Algorithm

After we rotate the image, the next operation is to make the separated clavicular cortex edges connected to each other. An edge is boundaries connected to each other which give specific characteristics to the shape of an object [5]. Therefore edge boundaries should be well connected.

The algorithm is performed like this:

Detecting segment of the clavicular cortex edge which is not connected

In detecting the unconnected segment, we find points that dont have another neighbor point from the directions of up, bottom, right, or left. Then we collect the coordinates of those points, so that we know the position of each unconnected point.

Figure 9: Detecting unconnected points Comparing an unconnected point to another unconnected point near it

After the positions of all points are collected, then we compare the first unconnected point to another unconnected point near it. For example, point 1 is compared to point 2, and then point 1 is compared to point 3, and then point 1 is compared to point 4, and so on. The comparation is the distance between the two points. The distance itself is resulted from computation with the function:

(5)

d = distance; = horizontal range;

= vertical rangeThis distance function has meaning: point which has more far distance horizontally is preferable than point which has more far distance vertically.

Figure 10: Physical explanation of the function Pick the next point which has nearest distance

The next step is to pick the nearest point to the first point, from the result of comparation operation.

Connecting the points (edge linking)

The final step is to connect two points that have the nearest distance. For example, after we compare point 1 to point 2, point 1 to point 4, and point 1 to point 5, then we gets the distance of each comparation, we find that the lowest value of the distance is from point 1 to point 5. Therefore, we then connect point 1 with point 5.

Figure 11: Edge LinkingThe result of all segmentation operation is given below:

(a)(b)(c)

Figure 12: (a) Image after Edge Detection Operation

(b) Rotated Image (c) Image after Edge Linking Operation3. Feature Extraction

The purpose of feature extraction operation is to measure the thickness of clavicular cortex. From segmentation operation, we have an image that only contains of the clavicular cortex. So the final step needed is to find the distance between upper cortex and lower cortex.

The distance is measured by subtracting the vertical position of point coordinates from each pixel in upper cortex with vertical position of point coordinates from each pixel in lower cortex.

(6)

In this research, we dont use all of the distance measurement. We only use three measurement sampling. The purpose is to give a better measurement result rather than use the average value of all point distance measurement. So we only measure the distance in three positions of point coordinates.

The result of distance measurement is still in pixel value, while the measurement result that we need is in millimeters value. So we have to perform certain conversion.

The conversion is performed like this:

The image resolution is 800 dpi

It means that 1 inch is equal to 800 pixels, or 1 pixel is equal to 1/800 inch.

So each pixel is equal to 25.4/800 mm

Therefore, we multiply the distance measurement result with 25.4/800

(a)(b)(c)

Figure 13: (a) Image after Edge Linking Operation

(b) Three measurement sampling (c) Final ImageResultsImplementation of the image processing algorithm to an independent software system through an implementation of a Graphics User Interface (GUI), will make the clavicular cortex thickness measurement easier, without using such a complex and heavy software like MATLAB.

The software system is implemented using Microsoft Visual C++ 6.0 software developer, using CxImage image library.

The specification of the system is:

Software system is compatible with Microsoft Windows 98 SE, Windows 2000, Windows ME, and Windows XP Operating System

Minimum PC criteria that should be used are Pentium 500 MHz, 32 RAM, and graphic card VGA 8 MB with monitor that can apply 1024768 screen resolution.

Input of software system is thorax x-ray digital image, using Windows Bitmap (*.bmp) format, 8 bit (grayscale) or 24 bit (RGB) intensity level, and having 32002400 pixel image size.

Output of software system is clavicular cortex thickness measurement in millimeters.

Software system is equipped with undo/redo operation, reset operation, and save operation.

Figure 14: The Software System

DiscussionIn this section, we will describe the experiment that is conducted to find the software system performance. The experiment is performed by comparing the software systems measurement results (the thickness of clavicular cortex in millimeters) with the T-Score resulted from prior patients examination using DEXA as the gold standard of osteoporosis diagnosing method.

Criteria used for analyzing the comparation result are:

Thickness measurement < 3mm is considered osteoporosis

Thickness measurement 3mm is considered normal

T-Score < -1 is considered osteoporosis

T-Score -1 is considered normal

Experiment is performed to 46 thorax x-ray image. The result of the experiment is shown in table 2.

Table 2: Experiment Result

From the experiment result, we could analyze the system performance. The performance is stated in five parameters:

Sensitivity

= 88.89 %

Specificity

= 90 %

Positive predictive value= 96.97 %

Negative predictive value= 69.23 %

Accuration Rate

= 89.13 %

ConclusionsIn this research, we develop a software system to measure the thickness of clavicular cortex, from an input of thorax x-ray image, as an early osteoporosis detection method. The software system could implement image processing operations such as: image contrast level enhancement, image noise removal, clavicular cortex edge detection, image rotation, edge linking, and image extraction (measuring the thickness of clavicular cortex). The software system could be used as osteoporosis screening method for early detection of osteoporosis.References

[1] SANKARAN B. (2000): Osteoporosis, (Novelty Printers, Mumbai)

[2] SARININGSIH. (2004): Osteoporosis diagnosis using clavicular cortex thickness measurement, compared with DEXA measurement, (Padjadjaran University Medical School, Bandung)

[3] HARVEY, SIMON, MD, Osteoporosis: http://www.adam.com

[4] NATHAN, M.H, M.D, Osteoporosis Diagnosed by Clavicular Cortex: http://members.lycos.co.uk/ jamusnet1/testfordad/index.htm

[5] JAIN, ANIL K. (1989): Fundamentals of Digital Image Processing, (Prentice Hall, Singapore)[6] DANUDIRDJO, DONNY. (2005): Design and Implementation of Osteoarthritis Diagnosis Software System by Detection of Knee Joint Stiffness, (Dept. of Electrical Engineering, ITB, Bandung) EMBED Visio.Drawing.11

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