2. Abstract Very important field of research in image processing is the ultrasound image processing. Because of the speckels, that are caused during the photo process, this is very difficult to handle the pictures without loosing information and/or spreading the edges. In this project we tried to improve serial of ultrasound pictures, by analyzing them and using statistical tools. In other words we tried to minimize the trade-off between smoothing the pictures and spreading the edges.

3. The problem the ultrasound machine is based on sending voice waves into the checked object, and translate the returning waves from the object. Due to the big number of scatterers that exist in the body of the object, every cell off the scanner receives many waves that come from different scatterers. This causes the manifestation which is called : THE SPECKLE NOISES.

4. The solution We found the speckles area by statistical parameter which is calculate in constant region of 11 X 11 pixels. (checking other constant region, has shown that the result that we get from the 11 X 11 region are good enough). As a primary operation we found the edges of the picture by using a known algorithm. This algorithm uses morphologic operations: Dilation And Erosion, and find edges using the following steps : 1. A= Dilation - Original 2. B= Original - Erosion 3. EDGE _ PIC = max(A,B)

5. From the EDGE_PIC we choosed threshold that defines an edge For each pixel in the original picture we moved in the 4 directions until we met edge. In the end of this process we defined a group (AREA) of pixels that exists in the same side of the edge. For those pixels we calculate average and variance and found a sub group D :

6. On this sub group of pixels we found the median value of pixels and gave the pixel this value. We did it for all the pixels in the picture. In the end of this process we activated a cross median filter to cancel singular noises that were not disappeared yet. This median was activated on small region ( 4 neighbors) so it has not hurt the edges. We made this all filtering process in about 50 times (iterations).

7. Block diagram Original pictureEdge picture Statistics (find area) Find sub group D Change the value of the pixel Cross median Iterations Final filtered picture { For each pixel

8. The tools MATLAB version 5.2 (very important).

9. Conclusions 1. The primary operation of finding the edges was very important to separate regions of pixels that are from different sides of the edge, so we could, in the end of the process,get more accurate value to the pixel. 2. The noises in the original picture were disappeared. 3. Regions that have seen to the eyes as connected were unioned in the end of the filtering process. 4. Repeating the all filtering process gave us better results, we needed 50 iterations to get very well filtered picture.

10. Inputs/Outputs originalmedian

11. original Filtered picture after 1 iteration

12. original Filtered picture after 50 iteration