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FILTERING ALGORITHMS FOR DE-SPECKLE THE ULTRASOUND IMAGES OF BONE FRACTURE

Ultrasound images are popularly known to contain speckle noise that degrades the quality of the images for good and fast interpretation in many areas of medicine, especially for bone fracture detection. This necessitates the need for robust de-speckling techniques for clinical practice. Therefore, a study was carried out to reduce speckle using filtering algorithms such as Wiener, Average, Median and Wavelets. This paper discusses the level of improvement obtained through these filtering algorithms using the peak signal to noise ratio (PSNR) as a measurement tool. The results of our work presented in this paper suggest that the combination of Daubechies–Wiener which we call as a hybrid technique, gave the best performance, which is a new contribution in this field. This de-speckling algorithm can be further developed and evaluated at a larger scale.


INTRODUCTION
Over the years, the field of radiology has grown as a result of advances in imaging technology. The most common imaging modalities used in orthopaedics are fluoroscopy (projection X-rays), computed tomography (CT) and magnetic resonance imaging (MRI).
However, these techniques use ionizing radiation, which raises important safety concerns for both patients and medical practitioners. Hence, it is important to find out and develop an alternative method for long bone fracture detection. The advantages of ultrasound imaging over Conventional radiography (X-ray), computed tomography (CT) and magnetic resonance imaging (MRI) are reported as being painless, does not use ionizing radiation, is less expensive, can be performed real time, needs no special environment and is clinically readily accessible. However, nonlinear characteristics of ultrasound, low signal-to-noise ratio, speckles, and reverberations make it exceedingly difficult to accurately and reliably determine the bone fracture [1].
Lazovic et al. [2] reported that out of 234 suspected apophyseal fractures in adolescents, 80 were detected by radiography and 97 by ultrasound. Rathfelder and Paar [3] described good results in the first substantial study on the possible application of ultrasound in the diagnosis of fractures in children. Hubner et al. [4] suggested that ultrasound assessment without radiography should be used in particular cases such as suspected bulge fractures or mildly displaced, simple fractures of the long bones of the forearm, humerus, femur, lower leg and clavicle. Evaluation by ultrasound for fracture detection has the advantage of immediate clinical correlation and can be made by people with minimal training in the use of ultrasound [5,6]. In addition, ultrasound images have the advantage of being noninvasive, portable, versatile, low cost and not requiring ionizing radiations.
However, ultrasound images in general are complex due to data composition, which can be described in terms of speckle information [7]. Upon visual inspection, speckle noise consists of a relatively high grey level intensity, qualitatively ranging between hyperechoic (bright) and hypoechoic (dark) domains [8]. Various researchers have used different filtering algorithms to improve the quality of ultrasound images.

MATERIAL AND METHODS
A bone fracture was captured using ultrasound equipment. Bone is a highly heterogeneous and anisotropic living tissue with composition and structure varying, depending on the skeletal site, function, age, sex and type of vertebrate species. Also, bone is a hard matrix of calcium salts deposited around protein fibers with ideal hardness, moderate elasticity, limited plasticity and brittleness for different body part movements.
The quality of the ultrasound images depends on the types of probe and how it is positioned, the experience of the examiner and the features of the ultrasound scanner itself. The images used in this study were obtained from [10] which used an Ultrasound Scanner of 3.5-9.5 MHz with a linear probe.  Figure 1 shows the process flow of the speckle reduction process. The five techniques were examined and compared to determine which one would be able to reduce speckle noise in ultrasound images. The filtering was implemented using MATLAB [11].
A total of 11 ultrasound images of bone fracture were used in this experiment. The original ultrasound images were processed separately using Wiener, Average, Median, Anisotropic Diffusion and Wavelets (Biorthogonal 6.8, Daubechies 10, Haar and Symlets 8) respectively, and the output images were generated. From the output images, the differences of images were analysed based on the peak signal-to-noise ratio (PSNR).

RESULTS AND DISCUSSION
The results of ultrasound image filtering carried out separately using Wiener, Average, Median, first, then, Wavelets (Biorthogonal 6.8, Daubechies 10, Haar and Symlets 8) are given in the following.        Figure 8 is Stress fracture of the tibia with callus formation. The PSNR values that were obtained for the ultrasound images used shown in Table 1. Table 1 shows that the best type of wavelet that could be used for speckle reduction in ultrasound images is Daubechies. It gives the highest PSNR value compared with the other type of wavelets. It can be seen that the wavelet-based method gets better results both in terms of speckle reduction and signal detail preservation. The wavelet filter is suitable for removing the speckle in ultrasound images and improving the image qualities as well. In addition, the method is easy to implement and the statistics are easy to estimate and characterize.         Overall, the results show that findings are consistent with Jin et al. [12] for Wiener filters, and Sudha et al. [13] for Wavelets. However, the results of our work presented in this paper suggest that the combination of Wavelet (Daubechies)-Wiener which we call as Hybrid technique gave the best performance in de-speckle the ultrasound images of bone fracture.

CONCLUSIONS
The paper discussed the investigation of speckle reduction of ultrasound images of bone fracture using various types of de-speckling algorithms such as Wiener, Average, Median and Wavelets (Biorthogonal 6.8, Daubechies 10, Haar and Symlets 8). The results show that amongst the Wiener, Average and Median algorithms, the Wiener gave the best results, and amongst the Wavelets (Biorthogonal 6.8, Daubechies 10, Haar and Symlets 8), the Daubechies yielded a better performance. Further, it was also discovered that the combination of Daubechies and Wiener gave better results compared to the individual filters. It is anticipated that future works would involve more experimental work with a variety of ultrasound images of bone fracture.