Automated Method of Fracture Detection in CT Brain Images

W Mimi Diyana W. Zaki1, M. Faizal Ahmad Fauzi2, Rosli Besar3

1.Department of Electric, Electronics and Systems, Faculty of Engineeringand build Environment, Universiti Kebangsaan Malaysia,,43600 Bangi, Selangor 2.Faculty of Engineering, Multimedia University,Persiaran Multimedia,

63100 Cyberjaya, Selangor, Malaysia 3.Faculty of Engineering and Technology, Multimedia University,

Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia 1wmdiyana@eng.ukm.my, 2faizal1@mmu.edu.my,3rosli@mmu.edu.my

Abstract

This paper presents a novel approach to automatically detect the fracture of skull in CT images. The approach consists of 5 steps: 1) skull segmentation, 2) skull extraction, 3) edge detection, 4) noise removal and, 5) image classification. Experiments show that the recognition rate is 99% for 100 images that are randomly chosen from a medical image database contributed by Hospital Putrajaya, Malaysia. This approach is simple and fast, but yet gives reliable results and high recognition rate. Due to this fact, it can provide a very strong basis of content-based medical image retrieval for medical training or diagnosis.

1. Introduction

The skull segmentation in medical images is an important step toward complete segmentation of the tissue in the human head, the latter being a significant step in the application of content-based medical image retrieval. Such application is useful for the purpose of medical training and diagnosis.

From CT images of brain or head, various structures of the brain can be examined to look for a mass, stroke, area of bleeding, or blood vessel abnormality. It is also sometimes used to look at the skull. One important abnormality in skull is a fracture or break in the cranial (skull) bonds occurred because of head injuries. A severe impact or blow can result in fracture of the skull and may be accompanied by injury to the brain. Broken fragments of skull can bruise the brain or damage blood vessels. If the fracture occurs over a major blood vessel, significant bleeding can occur within the skull, so head injury patients with

skull fracture have many more intracranial hematomas than those without fractures [1]. Hence, the automated detection of the skull fracture may help in identifying other abnormalities that might present in the CT brain image.

Unlike the rich literature in tumor brain segmentation [2,3], research on segmenting fracture skull from CT images is sparse. However, there are some authors work on segmentation of skull for the sake of construction of a realistic 3D model of the head [4]. H. Shao and H. Zhao had focussed on the segmentation CT brain for the sake of automatic diagnosis of the fracture skulls[5]. In their work, the region growing method whose seeds and growing criterions had been choosen using k-means before diagnosis rules based on information entropy was applied to the brain images. The method proved to give high recognition rate, however, its complexity and performance can be further simplified and computationally improved, respectively.

Due to this motivation, we have proposed an approach that automatically detects skull fracture in 2D CT images, which is a simple way that proves to give reliable results and high recognition rate. These advantages can provide a very strong basis of content-based medical image retrieval for medical training or diagnosis.

2. Methodology

Fracture in skull becomes one of important concerns for doctors in diagnosing other abnormalities in brain [1]. Thus, the automatic detection of skull fracture in brain segmentation system would provide useful indications of abnormalities that may present in CT brain images. The significant of this automated stage

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Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering

is shown in a block diagram of our proposed brain segmentation system, illustrated in Figure 1.

Figure 1. A general block diagram of the CT brain segmentation

The system is divided into a global and local segmentation. The global segmentation extracts skull and region of interest (ROI), which is intracranial and scalp from an image background. The local segmentation of fracture detection may help the system to identify the suitable ROI so that the proposed system would be more significant and useful. Due to this fact, this work has focussed on the automated method of fracture detection in CT brain images.

2.1. Medical image database

For this experimental work, we have randomly selected 27 fracture and 73 normal CT-scan images with 512 x 512 resolutions from a medical image database contributed by Putrajaya Hospital, Malaysia. Figure 2 illustrates an example of different slices scanned for the same patient. We focus on the image slices from the third ventricles below layer, x to the

cerebral cortex top layer, y to help radiologists in highlighting fracture from normal CT images [6].

Input: CT Head image

BackgroundHead

Scalp & Intracranial Skull/bone

Identify ROI Fracture detection

FractureScalp & Intracranial

Intracraniall

Automatic Thresholding Method

NO

YES Figure 2. Routine brain scout [6]

2.2. Automated fracture detection method

2.2.1. Image Segmentation. In this proposed approach, the Otsu’s thresholding method has been employed that based on a very simple idea to find the threshold that minimizes the weighted within-class variance,

. In this method, the thresholds are computed to maximize a separability criterion of the n -classes in gray levels [7].

)(2 tw

Let consider a 2D grayscale is thresholded into 2-classes that contains N pixels with gray levels from 1 to L , where a probability of a gray level, i in the image is given as

NifiP )( .

and is a number of pixel of gray scale i . The

within-class variance, can be then calculated as

if)(2 tw

)(22)(2)(2

1)(1)(2 ttqttqtw

where the individual class probabilities are estimated as

t

iiPtq

1)()(1 and

L

tiiPtq

1)()(2

and the individual class variances are

t

i tqiPtit

1 )(1

)(2)(1)(12 and

Global

Apply Sobel edge detection

Segmentation

Local Segmentation

Segmented image

Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering

t

ti tqiPtit

1 )(2

)(2)(2)(22 .

Besides, )(1 t and )(2 t are class means calculated as

t

i tqiiPt

1 )(1

)()(1 and I

ti tqiiPt

1 )(2

)()(2 .

For any given threshold, the total variance is the sum of the weighted within-class variances and the between-class variance,

)(,

2)](2)(1)][(11)[(1)(22

tBclassbetween

tttqtqclasswithintw .

Since the total invariance is constant and independent of t , the effect of changing the threshold is merely to move the contribution of two terms back and forth. Thus, minimizing the within-class variance is the same as maximizing the between-class variance.

2.2.2. Skull Extraction. During the experimental work, the whole image is categorized into classes, where background, soft tissues and skull are given with pixel values of 0, 0.5 and 1 respectively according to their intensity levels [5]. Due to the fact that the skull has the highest intensity value as compared to other segmented regions, the region of soft tissue with pixel value of 0.5 has been offset to 0 so that the skull can be successfully extracted.

3n

2.2.3. Edge Detection. Edge detection is an essential step in detecting skull fracture since it helps in deciding the result of final processed image. Using Sobel edge detector, a gradient operator of a 3x3 sample matrix is applied to find edges using the Sobel approximation to the derivative. It then returns edges at those points where the gradient of an image is maximum [8]. In this work, the threshold value of the calculated gradient magnitude is chosen heuristically in a way that depends on the input image.

2.2.4. Noise Removal. In the noise removal stage, the undesired high intensity pixels due to head pad or other noise pixels are removed. We have investigated that the undesired noise, mainly contributed by the head pad of the CT scanner could be successfully removed using the eight-connected neighbourhood, with fewer than 430 pixels.

2.2.5. Image Segmentation. In order to automatically detect the fracture skull from normal images, an image classification has been employed in this proposed

approach. The fracture images used in this work have only one skull fracture at a time. In the experiments conducted, the properties of image region are analyzed where the number of closed regions, in the segmented images is identified. If , the image is classified as fracture due to the only one closed region formed by a broken skull of the brain, as shown in Figure 6b. Besides, if

cn1cn

2cn , the image is classified as normal images, where there are two closed regions formed by its skull and it’s intra-cranial. It is illustrated in Figure 5b. Using different method of detecting fracture skull, the same justification has also been applied in [5]. This simple justification of classification method, yet shown to give excellent results has contributed to a shorter computation time of this proposed approach.

3. Experimental results and discussion

Table 1 shows experimental results obtained in this work. From the 100 images used, 99 images are correctly detected and classified, which gives the recognition rate, R of 99%. We define the recognition rate as follows

R= Number of images that correctly classified x100%

Total number of images used

Table 1. Experimental results Type of images

Number of images used

Number of images that are correctly classified

Normal 73 73Fracture 27 26

Figure 4b to Figure 4e illustrates the output images after all steps indicated in Figure 3 are one by one applied to the original image. It is clearly shown that a small fracture existed in the image has been successfully detected and classified as fracture skull.

Figure 5a and Figure 5b shows the results obtained for detection of normal image. Whereas, Figure 6a, Figure 7a and Figure 8a illustrate images with small, medium and large fracture respectively. They are among images with skull fracture that are correctly detected and classified.

The performance of the automatic fracture detection method has also been compared using four different edge detectors, which are Sobel, Canny, Roberts and Prewitt. The recognition rate obtained for the four different edge detectors are illustrated in Table 2. From the results, we notice that the fracture detection method using the Sobel edge detector gives

Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering

the highest recognition rate of 99%, followed by Canny, Roberts and Prewitt with 98%, 93% and 92% respectively. Due to this fact, the Sobel edge detector has been employed in this proposed method.

Table 2. Performance comparison for different edge detectors

Edge detector Recognition rate Sobel 99%Canny 98%

Roberts 93%Prewitt 92%

For comparison purposes, we have investigated the performance of automatic detection using k-means clustering. The experimental results give the same recognition rate of 99% which is same as using the Otsu’s thresholding method, however, with slower computation time. Performed with Pentium IV 2.0GHz CPU and 1GB RAM, the average computation time between the method that employed the Otsu’s thresholding and the k-means clustering are shown in Table 3. As we can see, method with the proposed Otsu’s thresholding performs 3 times faster than the k-means clustering.

Table 3. Computational time Automatic detection

methodAverage time taken to compute one image

Otsu’s thresholding 1.7964 sec K-means clustering 4.7867 sec

4. Conclusion and future work

In this paper, a novel approach is proposed to automatically detect skull fracture in CT brain images. The approach is simple and fast, besides proved to be reliable with high recognition rate of 99%. This approach has a bright future extension due to the recent development in computer vision and database management that motivates the application of medical image diagnosis in providing second opinions to doctors or radiologists.

The continuing refinement of algorithm should remains an important area of future work in order to locate the fracture and its size where the medical image retrieval to find similar cases for training and diagnosis purposes will be the coming steps in the research project. Besides, the CT brain segmentation for abnormality identification also becomes one of our ultimate goals, where this significant work may lead us to more promising results.

5. Acknowledgement

The authors are grateful to Faculty of Engineering, Multimedia University, Malaysia. We are also grateful to the MOSTI for their support under grant number 01-02-01-SF0014 (the eScienceFund Project), and to our collaborator, the Imaging Department of Hospital Putrajaya, Malaysia, which provides the CT brain images used in this work. Besides, we also would like to thank Dr. Fatimah Othman for her guidance.

6. References

[1] (2007) The U.S. Board Certified Physicians and Allied Health Professionals. [Online] Available: http://www.emedicinehealth.com/ct_scan/article_em.htm

[2] S. Loncaric and Z. Majcenic, “Multiresolution simulated annealing for brain image analysis,” in Proc. of SPIE Medical Imaging, 1999, p. 1139.

[3] Noel Pérez, José A. Valdés, Miguel A. Guevara, Luis A. Rodríguez and J. M. Molina, “ Set of methods for spontaneous ICH segmentation and tracking from CT head images,” in Proc. 12th Iberoamerican Congress on Pattern Recognition, 2007, p. 212.

[4] H. Rifai, I.Bloch, S. Hutchinson, J. Wiart and L. Garnero, “Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account,” Elsevier Science, Medical Image Analysis 4, pp. 219-233, 2000.

[5] H. Shao and H. Zhao, “Automatic analysis of the skull based on image content,” in Proc. International Symposium on Multispectral Image Processing and Pattern Recognition,2003, p. 741

[6] (2006) Radiographic Practice 3 – CT principle, parameters and protocols. [Online]. Available: http://www.mrs.fhs.usyd.edu.au/18363/Radiographic%20Practice%203%20week%208%20CT%202006.pdf

[7] N. Otsu,” A threshold selection method from gray-level histogram,” in Proc. IEEE Transactions on Systems, Man, And Cybernetics, 1979, p. 62.

[8] Y. H. Kuo, C. S. Lee and C. C. Liu, “A new fuzzy edge detection method for image enhancement,” in Proc. IEEE Int. Conference on Fuzzy Systems, 1997, p. 1069.

Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering

Figure 4b. The segmented image with 3n

Figure 4c. The extracted skull

Figure 4a. The original image

Figure 4d. The output image after the edge

detection

Figure 4e. The cleaned image after the noise

removal

Figure 5a.The normal original image

Figure 5b. The normal output image

Figure 6a. The original image of

small fracture skull

Figure 6b. The output image with detected

fracture

Fig 7a. The original image of medium

fracture skull

Figure 7b. The output image with detected

fracture

Figure 8a. The original image of large fracture

skull

Figure 8b. The output image with detected

fracture

Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering