Edge enhancement system for ultrasound images

A method for enhancing an ultrasound image. The image is assumed to be in the form of an ordered array of pixels. Each pixel corresponding to the intensity of the echo generated by a corresponding voxel in an ultrasound energy field. Axial and lateral directions relative to the ultrasound beam used to generate the sound field are defined within the ordered array. The method of the present invention includes the steps of dividing the image into a plurality of processing blocks, picking blocks that satisfy certain statistical constraints for edge enhancement, enhancing the pixels in the selected blocks, and then displaying the pixels in each block, whether enhanced or not, that are above a display threshold for the block in question. The statistical constraints are determined by second order statistics with reference to the axial and later directions. In one embodiment of the present invention, sum and difference histograms are used to approximate the second order statistics, thereby reducing the computational load.

FIELD OF THE INVENTION 
The present invention relates to ultrasound imaging systems, and more 
particularly, to a method for using a digital computer to enhance edges in 
ultrasound images. 
BACKGROUND OF THE INVENTION 
Medical ultrasound images are typically produced by generating an 
ultrasonic sound wave traveling in a known direction and observing the 
echoes created when the sound wave bounces off of boundaries between 
regions of differing density in the body. For any given direction, the 
image pixels are generated by plotting a dot whose brightness is 
proportional to the echo's amplitude at coordinate whose location is a 
function of the time the echo is received after a short pulse is send in 
the direction being measured. The process is repeated in different 
directions, thereby generating a two-dimensional image in which each point 
corresponds to the sound reflectivity of a different voxel in the 
patient's body. 
Ultrasound images typically include a significant amount of speckling which 
appears as random noise. This background makes the detection and 
enhancement of edges of organs and vessel boundaries difficult. Classical 
edge detection/enhancement techniques such as fixed size gradient 
operators or the Laplacian of a Gaussan operator are poorly suited to 
ultrasound images since these techniques also enhance the speckles and 
tend to widen the detected edges and speckles. 
Prior art edge enhancement methods for real-time applications rely on 
first-order statistics, namely the intensity histogram, to determine the 
boundaries of edges. Unfortunately, the properties of edges that are most 
effective in detecting edges require second-order statistics. Second-order 
statistics place a high computational load on the data processing system, 
and hence, have not been used for real-time applications. 
Broadly, it is the object of the present invention to provide an improved 
ultrasound imaging system. 
It is a further object of the present invention to provide an ultrasound 
imaging system with improved edge enhancement while not substantially 
enhancing speckling or widening the edges processed. 
It is yet another object of the present invention to provide an ultrasound 
imaging system in which the edge enhancement computations are within the 
computational capability of the computer systems normally included in 
commercial ultrasound imaging equipment. 
These and other objects of the present invention will become apparent to 
those skilled in the art from the following detailed description of the 
invention and the accompanying drawings. 
SUMMARY OF THE INVENTION 
The present invention is a method for enhancing an ultrasound image. The 
image is assumed to be in the form of an ordered array of pixels. Each 
pixel corresponding to the intensity of the echo generated by a 
corresponding voxel in an ultrasound energy field. Axial and lateral 
directions relative to the ultrasound beam used to generate the sound 
field are defined within the ordered array. The method of the present 
invention includes the steps of dividing the image into a plurality of 
processing blocks, picking blocks that satisfy certain statistical 
constraints for edge enhancement, enhancing the pixels in the selected 
blocks, and then displaying the pixels in each block, whether enhanced or 
not, that are above a display threshold for the block in question. The 
statistical constraints are determined by second order statistics with 
reference to the axial and lateral directions. In one embodiment of the 
present invention, sum and difference histograms are used to approximate 
the second order statistics, thereby reducing the computational load. In 
one embodiment of the present invention, a class of sigmoidal functions is 
used to remap the pixel intensities in the enhancement process.

DETAILED DESCRIPTION OF THE INVENTION 
The present invention utilizes a two step approach to edge enhancement. 
First, the image is segmented into processing blocks that include edges to 
be enhanced. The pixels of blocks having edges are then transformed via a 
sigmoidal transformation to enhance the edges. The sigmoidal function 
reduces the intensity of all pixels whose starting intensity is below the 
turning point of the transformation and increases the intensity of all 
pixels whose starting intensity is above the turning point of the 
transformation. 
To avoid enhancing blocks that do not contain edges, each block is examined 
statistically to determine if the block is likely to have an edge that 
should be enhanced. Blocks that contain only speckles or reverberation 
artifacts are not processed by the edge enhancement part of the algorithm. 
Blocks that contain strong edges or weak boarders, as defined below, are 
enhanced. 
The selection of blocks to be processed in the preferred embodiment of the 
present invention utilizes an approximation to the classical second-order 
statistics and a set of rules based thereon to identify the blocks having 
edges. If sufficient computational power is available, the second-order 
statistics in terms of the co-occurence matrix may be used instead of the 
approximation discussed below. Hence, the statistical measures utilized 
will be discussed both in terms of the co-occurence matrix and the sum and 
difference histograms utilized in the approximation used in the preferred 
embodiment of the present invention. The spatial gray-level co-occurrence 
matrix for a direction defined by the relative positions (k,j) and 
(k+.DELTA.x, j+.DELTA.y) in an array I.sub.k,j is given by 
##EQU1## 
where Card refers to the number of pixels for which the intensity of the 
pixel at (k,.lambda.) has intensity i and the pixel displaced from that 
pixel by (.DELTA.x, .DELTA.y) has intensity j. Here, N.sub.r is the number 
of gray levels with which the image is quantized. 
Similar information can be obtained at significantly less computational 
cost by using sum and difference histograms. It can be shown that the sum 
and difference of two random variables define the principal axes of the 
second order probability function of a stationary process. Further, for 
two independent and uncorrelated random variables, the joint probability 
function can be computed from the product of the probability function of 
the sum and difference variables. This observation allows the usual 
co-occurrence matrices to be approximated by their associated sum and 
difference histograms which can be estimated directly from the image. The 
error in this approximation is related to the degree to which the 
hypothesis of independence is satisfied. The sum and difference 
histograms, denoted respectively by P.sub.s and P.sub.d, are defined as 
follows: 
##EQU2## 
The features used to characterize blocks of the image may be written in 
terms of the co-occurrence or sum and difference histogram approximations. 
These features will be referred to as "texture parameters" in the 
following discussion. The texture parameters of interest in the preferred 
embodiment of the present invention are summarized in Table I below. 
##EQU3## 
The preferred embodiment of the present invention uses these statistical 
measures in the axial direction, i.e., along the ultrasound beam, and the 
lateral direction, i.e., at right angles to the beam direction. A block 
may be classified as having first-order reverberation effects, strong 
edges, and weak borders based on the texture parameters. 
The classification is carried out by first dividing the image into 
processing blocks, each with the dimension of m x n pixels. In each block, 
the texture parameters described above are computed for the axial and 
lateral directions. It should be noted that the texture parameters 
described above are depend on the displacement direction specified by 
(.DELTA.x, .DELTA.y). Hence, the "mean" corresponding to the direction 
dependent co-occurrence matrix is not the same as the mean from the local 
amplitude distribution as normally defined. The values obtained for the 
texture parameters are quantized such that decisions are based on ranges 
of values, referred to as Small, Medium, and Large in the following 
discussion. The small range is further divided into two ranges, the 
smaller of which being denoted by Very Small. 
The quantization is performed separately for the three texture parameters 
and each column of processing blocks. That is, the range of values for 
each texture parameter is computed for all of the processing blocks in a 
given column. The values in that column are then quantized into the three 
values for that texture parameter in the column. The process is repeated 
for each column. This procedure has the advantage of providing a 
self-calibration of the texture parameters. If the observed range of 
values for any texture parameter in any column is too small, the division 
of the small range into Small and Very Small may be omitted, i.e., only 
the Small level is used. Similarly, the classification of Large can be 
omitted if the range is too small. This situation arises when the blocks 
in the column all contain speckles or other uninteresting features. 
A block is defined to be a "first-order reverberation" block if the 
previous block in the axial direction had a mean, variance, and 
correlation parameters that were all Large and the current block has mean, 
variance, and correlation parameters that are all Medium. This reflects 
the observation that strong reflectors perpendicular to the scan beam 
generate reverberation artifacts which appear as edges just beneath the 
strong reflector. The reverberation edges appear to be similarly situated 
boundaries posterior to the actual reflector. However, these boundaries 
are less pronounced that the echoes from the real boundary. 
A block is defined to be a "strong edge" if the mean is at least Medium and 
the variance and correlation are Large in either the axial or lateral 
direction. 
A block is defined to be a "weak border" if any of the following conditions 
is satisfied in either the axial or lateral directions. First, the 
difference in variance and correlation between the previous block and the 
current block is at least Medium. Here, the blocks are ordered along the 
scan direction, the previous block being that corresponding to the image 
area nearest the ultrasound transducer. Second, the difference in the 
variance and correlation of the current block and the next blocks is at 
least Medium. Third, the correlation of the current block is greater than 
Vary Small, and the mean, variance, correlation of the previous block is 
Very Small. Finally, the correlation of the current block is greater than 
Very Small, and the mean, variance, and correlation of the next block is 
Vary Small. 
In the preferred embodiment of the present invention, blocks defined as 
having a strong edge or a weak border are enhanced using the edge 
enhancement algorithm described below. As noted above, this decision is 
based on the texture features computed for a specific direction. While 
strong reverberations are only found in the axial direction, strong edges 
and weak boarders may have any orientation. Hence, in the preferred 
embodiment of the present invention, the texture features are computed for 
both the axial and lateral directions. 
Before a block is chosen for edge enhancement, the preferred embodiment of 
the present invention adjusts the intensity of all of the pixels in the 
block to take into account the drop in intensity arising from the distance 
between the corresponding physical features and the ultrasound transducer. 
Objects that are farther from the transducer return smaller intensity 
echoes due to the dispersion of the ultrasound beam and the smaller solid 
angle subtended by the transducer for these more distant reflectors. 
Hence, blocks that are far from the transducer need to be increased in 
gain relative to those that are near the transducer. In the present 
invention, this is accomplished by applying a scaling factor to each block 
in an axial direction to correct for these distance effects. 
The scaling factor applied depends on the measured texture parameters as 
described above and the mean intensities of the blocks on the particular 
axial path. For the i.sup.th block, the scaling factor applied to the 
pixels is g .mu./.mu..sub.i, where .mu..sub.i is the mean of the block as 
computed above in the axial direction and .mu. is the average of the 
.mu..sub.i. The factor g depends on the texture parameters. If the mean 
and correlation are Very Small then g&lt;1, typically 0.6. Such a block is 
typical of an area of fluid or a cyst. If the mean and correlation are 
Large, g=1 to preserve the strongly reflecting feature in the block. 
The scaling factors computed above are assumed to apply at the center of 
each block. To avoid introducing a "blocking" artifact into the image, the 
actual scaling is computed for each pixel by linearly interpolating the 
scaling factors computed above and using the pixel's distance from the 
transducer. 
For blocks that are to be processed through the edge enhancement algorithm, 
the enhancement of the edges is carried out for each pixel in the block as 
follows: First, a small processing window, typically 5.times.5 pixels, is 
defined around the pixel to be altered and the mean of the intensity 
values of the pixels in the window is determined. In the preferred 
embodiment of the present invention, the mean value is used to select a 
sigmoidal-shaped transformation whose turning point is at the measured 
mean and which maps a pixel intensity of 0 to 0 and a pixel intensity of 
N.sub.r to N.sub.r, where N.sub.r is the maximum allowed pixel intensity. 
Other pixel intensity values are mapped such that values below the mean 
decrease in amplitude, and those above the mean increase in amplitude. 
Finally, the transformation curve is monatomic. These conditions assure 
that no pixel value will be altered such that it is out of bounds and that 
pixel values are not altered such that a first pixel that was less than a 
second pixel before enhancement is assigned a pixel intensity that is 
greater than that assigned to the second pixel after enhancement. It 
should be noted that the requirement that 0 be mapped to 0 and N.sub.r be 
mapped to N.sub.r are not necessary for the invention to function 
properly. 
The preferred sigmoidal curve is generated by using a conventional sigmoid 
function whose turning point is at the mean of the intensities in the 
5.times.5 window. Sigmoidal functions are functions of the form 
##EQU4## 
Here, x is the original pixel intensity and y(x) is the new pixel 
intensity. The parameter m is the "turning point" of the curve. Such a 
curve does not alter the values pixels whose intensities are at the 
turning point. As stated in Eq. (3), the curve does not satisfy the 
constraint of mapping 0 to 0 or N.sub.r to N.sub.r. Hence, the preferred 
embodiment of the present invention uses a curve which is sigmoidal at the 
mean of the intensities but is altered at one or both ends to provide the 
desired behavior for pixels that are either much lower than the mean or 
much higher. If the curve maps 0 to a value above 0 or N.sub.r to value 
below N.sub.r, the curve is replaced by a straight line tangent to the 
curve and passing through (N.sub.r,N.sub.r) if the condition created a 
pixel with intensity less than N.sub.r for an x=Nr. The replacement starts 
at the intensity value at which the point of tangency occurs. If the 
sigmoid fails to map an intensity of 0 to 0, the same strategy is applied, 
except that the tangent line connects (0,0) to the point of tangency below 
the turning point, and the portion of the sigmoid from 0 to the point of 
tangency is replaced by the straight line. 
If the mapping curve goes out of bounds, i.e., an x=N.sub.r is mapped to a 
new pixel intensity greater than N.sub.r or an x=0 is mapped to a negative 
pixel intensity, the basic sigmoidal curve is altered so that it remains 
sigmoidal near the turning point but is smoothly compressed between the 
turning point and the end point that was out of bounds. In the preferred 
embodiment of the present invention, the pixel transformation curves for 
each possible mean value are stored in digital form such that the 
enhancement transformation may be carried out by a simple table look-up 
operation. In principle, one such table can be stored for each value of 
the parameter "a" in Eq. (3). This parameter determines the degree of 
enhancement provided by the sigmoidal mapping. However, to save memory, 
the preferred embodiment of the present invention stores a table for only 
the smallest value of "a". If additional enhancement is needed, the 
transformation is iterated to provide the additional enhancement. 
As noted above, edge enhancement tends to broaden edges. To reduce any such 
broadening, the pixels of the processing blocks, whether enhanced or not, 
are only displayed to the viewer if the pixel intensity is above a 
"display threshold". The display threshold is computed for the entire 
block by selecting the a level which determines whether or not a pixel is 
actually displayed to the user. Pixels with intensities below this level 
are not displayed. The level may be selected with reference to the 
intregal of the intensity over the block or the intregal of the intensity 
squared, e.g. the power in the image in the block. In the preferred 
embodiment of the present invention, the level is set such that a 
predetermined fraction of the power or intensity intregal is not 
displayed. 
The above described algorithm may be most easily summarized with respect to 
FIG. 1 which is a flow chart of the preferred embodiment of an image 
enhancement according to the present invention. The image is loaded into 
the memory of the micro-processor or other computer as shown at 12. The 
image is divided into processing blocks and the texture parameters 
computed for each block in the axial and lateral directions as shown at 
14. The computed texture parameters are then quantized and normalized by 
column. Each block is examined according to the decision rules to 
determine if the block is to be subjected to edge enhancement as shown at 
16. Those blocks that are to be subjected to edge enhancement are 
processed by transforming the intensity of each pixel in the block using a 
transformation curve that depends on the mean of the pixels surrounding 
the pixel in question as shown at 18. A display threshold is then computed 
for each block and the pixels having intensity values above the display 
threshold are displayed on a CRT screen or in some other human perceivable 
form as shown at 20. 
As noted above, the preferred embodiment of the present invention also 
corrects the pixels for intensity variations that depend on the distance 
from the corresponding voxel in the body being imaged to the transducer. 
When such additional corrections are included, they are incorporated at 
the image blocking step 14 prior to computing the texture parameters. 
The preferred embodiment of the present invention is implemented on a 
general purpose computer of the types associated with conventional 
ultrasound imaging equipment. However, it will be apparent to those 
skilled in the art that other computational engines including engines with 
specialized hardware for computing the various statistical parameters may 
be used without departing from teachings of the present invention. 
Various modifications to the present invention will become apparent to 
those skilled in the art from the foregoing description and accompanying 
drawings. Accordingly, the present invention is to be limited solely by 
the scope of the following claims.