International Journal of Video& Image Processing and Network Security IJVIPNS-IJENS Vol:09 No:10 33 96510-2929 IJVIPNS-IJENS © December 2009 IJENS I J E N S Abstract — Ultrasound imaging has been introduced to provide a non-invasive and nondestructive technique either in industrial or medical field. In the medical field, ultrasound is broadly used for fetal and cancer disease detection and less for long bone fracture detection. This is mainly due to the formation of speckles in the ultrasound images which make the images unclear for fast interpretation. Therefore, a study was carried out to enhance the ultrasound images of long bone fracture. This involved image contrast enhancement and speckle reduction using filtering techniques such as Wiener, Average and Median Filters. This paper discusses the level of improvement obtained through these three filtering techniques. It was found through visual inspection and histogram analysis that amongst the three techniques, the Wiener Filtering is a better technique in reducing the speckle without fully eliminating the image edges. Index Term— ultrasound imaging, wiener filter, average filter, median filter, histogram equalization, contrast enhancement I. INT RODUCT ION ULTRASOUND or ultrasonography is a medical imaging technique that uses high frequency sound waves and their echoes. The technique is similar to the echolocation used by bats, whales and dolphins, as well as SONAR used by submarines. Ultrasound allows one to visualize and therefore examine a part of the human anatomy in medicine [1]. Ultrasound images in general are complex due to data composition, which can be described in terms of speckle information [2]. Upon visual inspection, speckle noise consists of a relatively high grey level intensity, qualitatively ranging between hyperechoic (bright) and hypoechoic (dark) domains [3]. In addition, ultrasound images have the advantage of being This work was supported in part by the Electrical and Electronic Engineering Department of Universiti Teknologi PETRONAS. Muhammad Luqman Bin Muhd Zain is with Electrical & Electronic Engineering Department in Universiti Teknologi PETRONAS. Bandar Seri Iskandar 31750 Tronoh Perak. Phone: +605-368-8000, fax:+ 605- 368-4075; muhammadluqman83@hotmail.com Irraivan Elamvazuthi is with Electrical & Electronic Engineering Department in Universiti Teknologi PETRONAS. Bandar Seri Iskandar 31750 Tronoh Perak. Phone: +605-368-7882, fax:+ 605-365-7443; (e-mail: irraivan_elamvazuthi@petronas.com.my). Mumtaj Begam is with Electrical & Electronic Engineering Department in Universiti Teknologi PETRONAS. Bandar Seri Iskandar 31750 Tronoh Perak. Phone: +605-368-7872, fax:+ 605-368-4075 mumtajbegam@petronas.com.my non-invasive, portable, versatile, low cost and not requiring ionizing radiations. Median filter has been introduced by Tukey [4] in 1970. It is a special case of non-linear filters used for smoothing signals. Median filter now is broadly used in reducing noise and smoothing the images. The filter preserves monotonic image features that fill more than half the area of the transform window. Based on Median filter, Hakan et al. [5] have used Topological Median filter to improve conventional Median filter. The Topological Median filters implements some existing ideas and some new ideas on fuzzy connectedness to improve, over a conventional Median filter, the extraction of edges in noise. The Topological Median filters defined are outperforming conventional Median filters with 7 x 7 or larger transform windows in reducing a noise while preserving edges. On the average, there is a minimal effect on edge strength or edge location. A conventional Median filter does outperform a Topological Median filter in the reduction of the amplitude of noise. Through experiments the variance of noise passed through a Topological Median filter was found to exceed that of a conventional Median filter by a factor of about 1.25. The better performance of the Topological Median filters over conventional Median filters is in maintaining edge sharpness, edge magnitude and edge location. Conventional Median filters reduce the variance of noise more than Topological Median filters. Xiouyin et al. [6] have presented an adaptive two-pass Median filter to remove impulsive noise. An image contaminated by impulsive noise is represented in a two-pass Median filtering and is processed by a Median filter twice. By analyzing the spatial distribution, i.e., the error index matrix of the impulsive noise, the adaptive two-pass Median filter looks for columns containing over-corrected pixels by the standard Median filter and replaces over-corrected pixels by their original values. The experiment that has been done shows that the adaptive filter is able to reduce the mean square (MSE) and mean absolute error (MAE) produced better results. Yanchun et al. [7] proposed an algorithm for image denoising based on Average filter with maximization and minimization for the smoothness of the region, unidirectional Median filter for edge region and Median filter for the indefinite region. It was discovered that when the image is corrupted by both Gaussian and impulse noises, neither Average filter nor Median filter algorithm will obtain a result good enough to filter the noises because of their algorithm. The image is divided into different regions using neighborhood contrast intensity and employ different methods to denoise the pixels in different regions. This is not only to Enhancement of Bone Fracture Image Using Filtering Techniques Muhammad Luqman Bin Muhd Zain, Irraivan Elamvazuthi and Mumtaj Begam