Evans, A.N.

Nixon, M.S.

English

Southampton (e-Prints Soton)

44 SPECKLE FILTERING IN  ULTRASOUND IMAGES FOR FEATURE EXTRACTION A N Evans and M S Nixon Department of Electronics and Computer Science, University of Southampton, UK INTRODUCTION In  ultrasonic imaging, speckle degrades  images by concealing fine structures and  reducing  the signal to noise ratio (SNR).  The theoretical  aspects  of speckle, both laser and ultrasound, have been dealt with  by  several  authors  including  Goodman  [7], Burckhardt, [3] and Wagner (151, although it should be  noted  that in any practical ultrasound  imaging system  effects such  as nonlinearities and envelope smoothing can result  in  deviation from theoretical models (Tuthill ei al[13] and Waag et al[14]). Removing speckle improves the SNR and provides a more suitable basis for computer based image in- terpretation using feature extraction techniques, for example the work of  Nixon et al [ll]  and Helin and Ayache [SI.  The effect of  a speckle reduction  tech- nique on the position of  edges is of  critical import- ance if the resulting image is to be used  as an input to a feature extraction stage. Various techniques have been applied to reduce the speckle content of  ultrasonic images including com- pounding in which multiple images taken from vary- ing angles, or with different centre frequencies, are averaged and phase-based methods after Forsburg et a1 [6] where phase and amplitude characteristics are directly manipulated in individual returning A-lines. There are many established image processing tech- niques  that  can  be  used  to  reduce  the  noise  in images of  which  the median  filter  is  arguably the most popular nonlinear method. However, although it  successfully removes speckle noise it  also mod- ifies  edges  present  within  the  image.  For  fully developed speckle,  when  the number  of  scatterers present  within  the imaging system  resolution  cell is  large,  the image statistics are constant  but  as the number of  scatterers decrease and at boundaries between features the local statistics vary and can be used to control the degree of filtering. The ability to filter heavily in uniform regions while preserving edges is found in the adaptive class of fil- ters, resulting in the adaptive weighted median filter of  Loupas  et al [lo] and the unsharp masking filter of Bamber and Daft [l].  These filters use local image statics to vary the output between the original value and the mean or median of  a local region.  In both cases the ratio of  the local mean to the local vari- ance controls the proportion of each, with the output tending towards the original value where the ratio is low, ie at an edge, thus improving the performance of  the filter at discontinuities. Unfortunately as the complexity of the filter increases so does the compu- tational cost, motivating  the development of special- ist hardware (Loupas [9] and Bamber and Phelps [2]) if a real-time implementation is to be achieved, which is desirable as ultrasound itself is a real-time imaging system. In addition, both filters require prior choice of a controlling parameter which in practice requires an initial pass of the filter over the image. Although for series of  similar images a common constant can be  used,  these  adaptive filters  require some degree of  user interaction and are not therefore completely automatic. This paper introduces use  of  the truncated median filter of  Davies [5] to filter ultrasound  images, con- trasting the results achieved with those of other cur- rent  filters  for speckle reduction.  The filters  used for  comparison  were  a standard median filter,  the adaptive median filter  [lo] and the adaptive unsharp masking filter [l]. The motivation behind this research is to determine whether a simple non-adaptive filter can achieve ac- ceptable results with a lesser degree of computational complexity than more advanced adaptive filters with fully automatic implementation. FILTERING TECHNIQUES The  weighted  median  filter  extends  the  simple concept of  the median filter  by  the introduction of weight coefficients. This operation is performed by multiplying each term of  the ranked pixel intensity values, {fl, fz, . . .  f~},  by  its respective weight, de- noted w~,  w~,  .  . .  w~,  to form an extended sequence {wlfi,wzfz.. .wN~N}.  For a square window with sides of length 2K + 1 the weight coefficient w;,j at position (ij)  is given by: where  W(K+~,K+~)  is  the  central  weight  and  the square  brackets,  [  1, denote the nearest  integer to Acoustic Sensing and Imaging, 29  - 30 March 1993, Conference Publication No. 369, 0 IEE, 1993 45 the term inside the brackets or zero if  the result  of the addition within them is negative;  c is a scaling constant and d is the distance of the point from the centre of the window. The local mean is denoted by ? and the local variance by u2. In  the adaptive mean-based  filter  the  measure  of similarity, p,  given by  the ratio of the local variance over the local mean, is used  to control a constant k such that 0 5 k 5 1 by where  p.  is  the  mean  value  of  p  for  a local  area of  an image that  is  considered  to consist  of  fully developed speckle. The constant g is a scaling factor that ensures k is always less than one. K adaptively varies  the output 2  between  the original  value  (2) and the local mean (Z), such that i =  f +  k(2 -  Z) THE TRUNCATED MEDIAN FILTER The truncated  median filter  was  first  proposed  by Davies  [SI and approximates to a modal filter.  The most frequent value in any distribution is the mode, but the difficultly of determining the mode of a small population,  such as that contained  within a 9  x  9 mask has meant that whilst the median filter enjoys great  popularity  the modal  filter  has  largely  been ignored.  Near  edges  the modal filter  will suppress noise, where little is present, or enhance the edge in the case of  high  noise.  When directly  on  an edge the  mode can  be  either  one  of  two  peaks  as  the population is essentially bi-modal and this can result in  rather  coarse decisions  being taken.  The mode thus appears to offer a improvement over the median filter near to edges but may give erratic results when directly on the edge. The order of the mode, median and mean can be used to estimate the mode for many distributions includ- ing those of  ultrasound images.  Figure 1 illustrates that  if  the median  is  moved  away  from the  mean then it will approximate to the mode.  To move the median, Davies proposed to  truncate the original fre- quency distribution graph so that the median bisects the range of  distributions  and a new  median value found  that is  closer  to the mode.  This process  is iterative, each pass of the filter moving the truncated median output further towards the mode, although in practice one pass has been  found to be sufficient. Additionally,  if  the  filter  is only  applied  once  the output errs towards the median thus overcoming the problem of  the mode being too harsh at edges. Figure  1:  Normal  Order  of  Mean, Mode and Median  in a Unimodal  Distribution and Method of  Truncation EXPERIMENTAL RESULTS The four speckle reduction filters were applied to  test images using a 9 x 9 mask. To allow accurate evalu- ation of  the truncated  median  filter  in  comparison with  contemporary  techniques  not  only  subjective but  also objective differences need  to be  appraised. One  such  quantitative  measure  is  the Contrast to Speckle Ratio (CSR)  of Patterson and Foster  [12], a measure of the observer’s ability to observe a void or dark  area against a background  of  speckle, defined as where 5;  and U;  are the average signal value and the variance respectively  inside the void and  fo  and U,” are those outside the region. In addition to  the CSR, local SNR  measurements were also taken, defined as SNR  = E where  f and U denote mean and standard deviation respectively.  Both  the CSR and SNR  were calcu- lated over a 9 x  9 region, matching the filter  mask size.  The effect  of  filtering  on  the  position  and strength of edges present was evaluated by the use of the Canny edge detector  [4].  However in an in IJWO image, correct position of the edges is not known and it is  therefore  impossible to determine whether  the edges have been modified by the filters. To overcome this limitation a circular pipe in a water bath, mod- elling an artery, was used as a phantom providing a real B-scan image containing an object of known size and shape.The pipe used  was hard  plastic, 250” long and 28”  in diameter.  With an ideal imaging system the B-scan  image would contain a complete circle corresponding to the pipe.  The actual image only approximates to this but as the exact position of  the edge  is  known  this  provides a  standard for comparison of  the filtered  results. 46 Filter Type  Median CSR  18.3 1 Truncated  Adaptive  Unsharp Median  Weighted  Masking 15.3  18.3  10.5 Improvement (dB) SNR On Edge Improvement (dB) SNR  Off Edge Improvement (dB) Figure 2: CSR  and SNR  of  In Vivo Image Figure  3  (opposite):  In  Vivo  Images  (from  top  to bottom): (a) original, (b) median filtered, (c)  truncated median filtered,  (d) adaptive  weighted  median  filtered, (e) unsharp masking filtered 21 0  19 4  21 0  16 2 1 63  126  137  2 31 1 5  -0 7  -0 03  46 73 7  62 9  73 7  44 6 20 2  1s 9  20 2  15 9 In Vivo Test Image Figure 3  presents  the original  and filtered  images of  an in  vivo scan of  the carotid  artery.  Cosmetic improvement to the original  image is  achieved  by all the filtering techniques although the edge blur- ring by  the unsharp masking filter can be observed at the  artery/tissue  junction  in  figure  3  (e).  In the median and adaptive median filtered images the edges around the artery are clearly defined with the strongest edge being displayed be the truncated me- dian filter. The adaptive weighted filter clearly pre- serves features best and has been  able to highlight small structures  removed  by  other techniques,  see for example the dark vein in  the top left corner of figure 3 (d). The Contrast to Speckle Ratio (CSR)  and Signal to Noise Ratio (SNR)  are used to interpret the results, figure 2. Here the CSR  is calculated using the centre of  the artery  to calculate  3, and  uf  and an area consisting solely of  speckle to calculate it,  and a;; the SNR at an edge was found at the tissuelartery boundary.  The unsharp masking filter consistently offered the smallest improvement in CSR  and SNR away from an edge and also showed the largest im- provement in SNR at an edge, indicating the most edge modification. The  CSR  and SNR  away from an edge are improved by 21.0dB and 20.3dB respectively by both the me- dian and adaptive median filters, with the truncated median filter offering a slightly lower  improvement in each case. At an edge the median filter preserves 47 g-ilter T~~~ NumberolMatchingEdgePoinfs TotolNumberoJEdgePoints Median Truncated Median Adaptive Weighted Unsharp Masking Original Figure 4: Ratio of Number of Edge Points Matching Ideal Edge to Total Number of  Edge  Points 25.8 28.1 __ 25.3 12.6 4.9 least  edge information (i.e.  the greatest change in SNR), then the truncated median filter and the ad- aptive weighted  filter and the appears to give  the best  edge preservation, with virtually no change to the SNR. The ideal edge model is not known but by observation of the edge detected  images in figure 5, conclusions can be drawn about the edge preserving properties of the filters. The adaptive weighted  and truncated median filters have preserved  more complete edges than the other techniques, for example the detail to the left of  the artery in the figure 5 (b) and (c) which is not present in the other images. The adaptive median technique shows the greatest edge detail, as would be expected from its nature. Although the truncated median fil- ter has less edge detail, those edges that are present are strongly  defined  and continuous, comparing fa- vourably  with the median filter.  Fewest  edges are determined by the unsharp masking  filter, illustrated in figure 5 (d) and those that can be seen are rough and insubstantial. Phantom Test Image The edge detected  images overlayed  with the edges expected  from an ideal imaging system in figure  7 show  a close coupling between ideal and actual res- ults although figure 7 (d) shows edges due to speckle that have been  removed by the other techniques, il- lustrating the disadvantage of mean based filtering. To  quantify how  close  the resulting  images are to the overlayed image, the ratio of the number of edge I  I Figure  5:  In  Vivo  Images  Edge  Detected  (from  top to bottom):  (a) median  filtered,  (b) truncated  median filtered,  (c)  adaptive  weighted  median  filtered,  (d) unsharp  masking filtered 40 a fully automated mode with no requirement for the user to preset a constant value, offering an advantage over the adaptive class of filters. Acknowledgements The authors  would  like  to  thank T.K. Hames of Southend Hospital for his advice and suggestions  and SERC for the funding that made this work possible. Relative Time Figure 6: CPU Times for  Filtering Techniques points  that correspond  to points in  the ideal edge image to the total number of edge points was taken for  each  edge  detected  filtered  image, the  results being tabulated  in figure 4.  The truncated median technique shows  the highest  percentage,  then  the median filter which is just above that of the adaptive weighted median filter.  The unsharp masking filter performs considerably  worse  than  the  other  three methods. Processing Times The relative processing time for each technique, im- plemented is software, is used  as a measure for com- parison,  see  figure  6.  Although  these  figures  are obviously implementation dependent it can be seen that relative to the fastest technique the median and truncated median filters have similar values while the adaptive weighted median filter’s complex nature is reflected in its long processing time. CONCLUSIONS The truncated median filter has been applied to ul- trasound images and its performance evaluated using qualitative and quantitative methods, based  on  in vivo and phantom test images. The use of a phantom to model the carotid artery provided  an exact basis for comparison of filtered edge data with edge data derived from the raw images. It has been  found that the truncated  median filter improves upon the performance of the median filter, especially  at edges,  with  little increase  in  compu- tational  cost  and  therefore  appears  very  suited  to become part of  the stock of  tools for speckle reduc- tion  for  enhanced  image analysis.  The smoother, more continuous edges produced  hy  the truncated median filter are particularly attractive if a further computer vision stage, such as feature extraction, is to be  applied.  The filter result at the arterial wall surpasses that of the median filter and the adaptive unsharp masking filter, highlighting the deficiencies of mean-based filtering, and is comparable with that of  the adaptive weighted  median filter  without the computational expense  afforded  by  adaptivity.  In addition the truncated median filter can he used  in REFERENCES 1  Bamber, J., and Daft, C. 1986. Ultrasonics. 41-44 2  Bamber, J., and Phelps, J. 1991. Ultrasonics. 218-224. 3  Burckhardt, C. 1978. IEEE Trans Son U1. 2.  1-6 4  Canny, J. 1986. IEEE Trans PAMI. 1.  679-698. 5  Davies, E. 1988. Pattern Recognition Letters. 7. 87-97. 6  Forsberg, F., Healey, A., Leeman, S..  and Jensen, J. 1991. Phys Med Bio. 36. 1539-1549. 7  Goodman, J. 1976. J Opt  SOC A. E.  1145-1150 8  Herlin, I., Ayache, N. 1992.  Image and Vision Comp. 673-682. 9  Loupas, T. 1988. PhD thesis, University of Edinburgh 10  Loupas,  T.,  McDicken,  W.,  and  Allan,  P.  1989. IEEE Trans Cir Sys. 36.  129-135. 11  Nixon,  M.,  Hames,  T., Martin,  P.,  Powell,  S., and de  Paiva,  M.  1992.  In  IEE Image Processing and its Applications, Maastricht, 373-376. 12  Patterson, M., and Foster, F. 1983.  Ultrasonic Imaging. 5.  195-213. 13 Tuthill, T., Sperry, R., and Parker, K. 1988.  Ultrasonic Imaging. g.  81-89. 14  Waag,  R.,  Demczar,  B.,  and  Case,  T. 1991.  IEEE Trans Biomed Eng. 38.  628-633. 15  Wagner,  R., Smith, S.,  Sandrik, J., and Lopez, H.  1983. IEEE Trans Son U1. 3.  156-163. 49 Figure  7:  Filtered  Phantom  Images  Edge  Detected  (black) and  Ideal  Image  Edge  Detected  (grey):  (a) median,  (b) truncated median, (c) adaptive weighted  and  (d) unsharp masking 

Speckle Filtering in Ultrasound Images for Feature Extraction

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