Image facsimile with real time image segmentation

A method and system for remote diagnosis of a radiograph includes remote locations connectable to diagnosis locations. At a remote location a radiographic image containing a part depicting tissue and a background part is obtained and digitized. The image can be considered to comprise a plurality of blocks. A block of the digitized radiographic image is segmented to obtain a part of the block which depicts mainly tissue. The part of the block is compressed and transmitted to a diagnosis location. This segmenting, compressing and transmitting of blocks is repeated until the entire part of the image which depicts tissue has been transmitted to the diagnosis location. For an image, the digitization, segmentation, compression and transmission can be performed in a pipelined fashion. optionally computer assisted diagnosis (CAD) can be performed on the digitized image. The diagnosis location receives the compressed segmented image and any CAD results; uncompressing the received image; combining the CAD results with the uncompressed image; and displays the uncompressed image and the CAD results.

FIELD OF THE INVENTION 
This invention relates to teleradiology, and, more particularly, to 
teleradiology of mammograms using real-time, on-the-fly image 
segmentation. 
BACKGROUND OF THE INVENTION 
Teleradiology is the process of sending radiologic images from one point to 
another through digital, computer-assisted transmission, typically over 
standard telephone lines (POTS), or over a wide-area network (WAN) using 
dial-up ISDN lines or other switched digital services. Using 
teleradiology, images can be sent from one part of a hospital to another 
part of the same hospital, from one hospital to another, from remote sites 
to diagnostic centers, etc. In other words, images obtained at one 
location can be sent to almost any place in the world. 
As cost-effectiveness in medical diagnostic imaging becomes a major issue, 
teleradiology (remote radiology or the transmission of radiologic images) 
is becoming an acceptable way to make diagnoses and to consult with 
referring physicians. Teleradiology has been called the "great equalizer 
for radiology" and it has allowed normal practice limitations like 
distance, licensure and reimbursement to be largely eliminated. 
Computer-assisted transfer of digitized images allows geographically 
dispersed consultants to lend their expertise to remote regions, thereby 
benefiting patients who now may have limited access to radiological 
services. Teleradiology systems especially are important to rural medical 
facilities. 
Teleradiology requires trade-offs of image quality (that is, image quality 
sufficient to perform accurate diagnosis) with system cost and image 
transmission time. 
Although some teleradiology systems have been implemented using standard, 
off-the-shelf equipment, effective teleradiology typically requires 
expensive, specialized equipment as well as persons trained in its 
operation, maintenance and use. As a consequence, in remote locations 
where teleradiology would be of most use, it is unlikely to be readily 
available. As noted in a recent article on the subject, "[a]lthough 
technological advances continue to drive decreases in system prices, 
teleradiology and telemedicine continue to face significant challenges." 
17/2 Health Management Technology Feb. 22, 1996. 
Low-cost teleradiology systems developed using standard, off-the-shelf 
components suffer from various problems. For example, in one system "the 
image digitization time . . . was quite long." Low cost digital 
teleradiology, Reponen J. et al, 19/3 EUR. J. RADIOL. 226-231, 1995. 
Radiographic images (X-rays) typically contain vast amounts of information. 
It is therefore desirable to be able to compress the images, especially in 
a teleradiology system where the images are to be electronically 
transferred to remote locations in a reasonable amount of time. 
In many radiographic images, areas which do not depict tissue or other 
regions of interest (ROI) may be eliminated to reduce the amount of data 
managed by systems and transferred between systems. Segmentation or 
partitioning of an image may also enable more efficient data compression 
of the image. Segmentation schemes using complete images have been used. 
In the specific area of telemammography, results have been mixed. One study 
concluded that further improvements in hardware and imaging parameters may 
improve detection of soft tissue abnormalities and that further evaluation 
is necessary to determine whether teleradiology might be applicable to 
breast cancer screening. Detection of breast abnormalities on 
teleradiology transmitted mammograms, Fajardo L. L. et al., 25/10 INVEST. 
RADIOL. 1111-1115, 1990. 
It is therefore desirable and useful to provide teleradiology for 
mammograms which achieves a good balance of the quality/cost/time 
trade-off. That is, it is desirable to provide teleradiology for 
mammograms using standard imaging, computer and communication equipment 
while still achieving acceptable results. 
It is also desirable to segment images in real-time (or almost real-time) 
as they are obtained from an image acquisition system (for example, a film 
digitizer). 
SUMMARY OF THE INVENTION 
In one aspect, this invention is a teleradiology system which overcomes 
problems with the above-mentioned systems by providing a low-cost 
teleradiology system which produces high-quality images in almost real 
time. 
In another aspect, this invention provides two independent image 
segmentation schemes to segment images from a limited data set. The two 
schemes can be used in combination to compare results and improve 
reliability. 
These segmentation schemes are able to operate in real-time (or almost 
real-time), segmenting images obtained from an acquisition system based on 
a limited amount of information such as eight lines or columns of image 
data. 
In one aspect, this invention is a method for remote diagnosis of a 
radiograph. The method includes, at a remote location: obtaining and 
digitizing a radiographic image containing a part depicting tissue and a 
background part, wherein the image can be considered to comprise a 
plurality of blocks; segmenting a block of the digitized radiographic 
image to obtain a part of the block which depicts mainly tissue; 
compressing and transmitting the part of the block to a diagnosis 
location; and repeating the segmenting, compressing and transmitting of 
blocks until the entire part of the image which depicts tissue has been 
transmitted to the diagnosis location. 
In another preferred embodiment, the method further includes, at the remote 
location: optionally performing computer assisted diagnosis (CAD) on the 
digitized image; and transmitting the results of the CAD to the diagnosis 
location.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EXEMPLARY EMBODIMENTS 
A teleradiology system 10 according to the present invention includes 
various sites, namely one or more remote sites 12 and one or more 
diagnosis sites 14. Each remote site 12 is connected to at least one 
diagnosis site 14 via communication channels 16. The network of sites can 
be a LAN or a WAN or communication over channels 16 can be performed by 
communications devices (e.g., bridges, modems and the like) 18, depending 
on the system configuration and the location of the sites. The network of 
sites can be any combination of LANs, WANs and other networks or 
communication links in which a remote site 12 can communicate with a 
diagnostic site 14. 
A site may have more than one communications device 18. Practical 
implementation is through an integration of routing, bridging and 
switching functions. Typically a remote site 12 has one communications 
device such as a modem 18 and connects to a single diagnosis site whereas 
a diagnosis site 14 receives images from more than one remote site 12 
using one or more modems. 
Each remote site 12 includes a film digitizer 20 and a CAD system 22, both 
connected to a quality assurance (QA) display and text entry system 24. 
The CAD system 22 and the QA system 24 are connected to the site's 
communications device 18. 
A diagnosis site 14 includes a communications and image processor 26 
connected to an image archive database 28, and image display 30 and an 
output device such as a printer 32. The processor 26 is connected to the 
site's communications devices 18. 
In presently preferred embodiments, at a remote site 12, the film digitizer 
20 is a Lumisys FD 150 digitizing at 50 .mu.m resolution during a 50 
second process. The QA system 24 is a Gateway 2000 P5-90 with a 15 inch 
Vivitron monitor, and the modem is a Microcom Deskporte 28.8 Kbps modem or 
a Zyxel Elite 2864 28.8 Kbps modem. 
At diagnosis sites 14, the communications device 18 is the same as at the 
remote sites (a 28.8 Kbps modem) and is compatible with remote 
communications devices 18. The printer is a Kodak HR 2180 laser printer 
capable of printing an image with a resolution of 43 .mu.m. 
Operation 
In presently preferred embodiments, the present invention operates on 
images such as the mammogram 34 of FIG. 2. Mammogram 34 consists 
essentially of three parts: a soft tissue part 36 depicting breast tissue, 
a background part 38, and patient identification information 40. The 
patient identification information 40 is typically added to the mammogram 
34 at the time the image is formed or processed and is used to associate 
the mammogram with a particular patient. The information 40 can be, for 
example, textual, barcodes or combinations thereof. 
In operation, the system 10 works as follows (with reference to FIGS. 3 and 
4): 
A radiograph (e.g., mammogram 34 including patient identification 
information 40) is produced at a remote site 12 using some appropriate 
device (not shown). This radiograph is then digitized (step S20) using 
film digitizer 20 to produce a digital version of the radiograph which can 
then be checked for quality control by QA display and text entry system 
24. Next the patient identification information 40 is found, removed from 
the image and saved (step S22). This finding and removal can be done by an 
operator or by a program which searches for the region. 
A typical digitized mammogram comprises about forty megabytes (.about.40 
MB) of data. The present digitizer takes about fifty (50) seconds to 
digitize an image. 
Once the image has been digitized, processed by the QA system, and the 
identification information has been removed and saved, the image is then 
segmented by removing the non-tissue areas (step S24). The segmentation 
process is described in greater detail below and reduces a typical digital 
mammogram to between about five and fifteen megabytes of data. 
The image can then optionally be subjected to computer aided diagnosis 
(using the CAD processor and display 22) (step S26). If CAD is performed, 
various so-called regions of interest (ROIs) are located on the digitized 
image. These correspond to regions which depict potential problems in the 
tissue or bone (hard tissue) depicted in the radiograph. For example, in 
the case of mammograms, the ROIs could depict masses or clusters of 
microcalcifications. 
Presently preferred embodiments of this system 10 operate on digital 
mammograms. In these embodiments, the CAD scheme is one or more of the CAD 
schemes described in U.S. patent applications Ser. Nos. 08/352,169, 
08/556,814 and 08/613,363, the entire contents of which are hereby 
incorporated herein by reference. 
In some preferred embodiments, a digital signature of the segmented image 
and the patient identification information 40 is determined (step S28) and 
saved. Any well-known message digest algorithm or mechanism can be used to 
determine this digital signature. 
The tissue region of the image is then compressed on the fly (step S30) and 
transmitted to a remote diagnosis site 14 (step S32). The process of on 
the fly compression is described in greater detail below. Compression of 
the tissue region reduces it to between about fifty to six hundred 
kilobytes (.about.50-600 KBytes). Transmission rates are about 2 
KBytes/sec via POTS or faster by ISDN. 
Once the image has been completely transmitted, the previously saved 
patient identification information 40 (saved in step S22) is sent along 
with any CAD results and signatures (if steps S26 and S28, respectively, 
are performed). 
At the diagnosis site 14, the image, patient identification information and 
any other data (e.g., signatures and ROIs) are received (step S34), the 
image is uncompressed on the fly (step S36) and is padded to insert the 
non-tissue regions (step S38). 
If a signature was determined at the remote site (step S28) then this 
signature is checked to ensure that the image and the patient information 
40 match and are valid. 
Next, the patient identification information 40 is added back into the 
image (step S42) and optimized lookup tables (described further below) are 
set (step S44). These tables match optimal densities in the original image 
at the remote locations with the final image as displayed or printed onto 
film to assure that the "look" and "feel" of the final image is the same 
as that of the original tissue portion of the image. 
Finally the image is displayed on image display 30 (step S48) with the CAD 
results (if any are present) displayed by overlay or side-by-side with the 
image (steps S46). The image can also be printed on printer 32 and stored 
in the image archive database 28. 
A practitioner can then view the image and perform any appropriate 
diagnosis. The image processor 26 at the diagnosis site can also perform 
CAD on the image and display these results on the image display 30, 
The segmentation step at the remote site 12 is now described in greater 
detail with reference to FIGS. 5 and 6. Two independent segmentation 
schemes are included. 
Segmentation scheme 1 
The first segmentation scheme relies on the fact that x-ray images, 
radiographs in general and mammograms in particular, have the highest 
intensity of absorbed x-ray photons just beyond the tissue boundary (that 
is, the skin line). 
Accordingly, if one searches for changes in digital values (that are 
proportional to signal intensity) along a data line that crosses from 
tissue to non-tissue areas, then the maximum signal will be just outside 
the skin line. This is because it contains an area of full direct exposure 
(that is, no tissue to attenuate primary x-rays) and also scatter 
radiation from the near tissue. 
FIGS. 9A-9B depict one such signal line. This maximum signal location can 
be used as an indication for the segmentation location. 
With data from several lines of an input (digitized) image, a continuous 
smoothed line of segmentation with appropriate shape (for example, 
convexity) can be constructed on the fly as image data arrives from the 
acquisition device. 
Segmentation scheme 1 operates as follows (with reference to FIGS. 5-8): 
First, the top edge 44 (FIG. 7) of the digitized image 34 is located (step 
S50). This is done by scanning each successive line of the image (starting 
with the first line) for pixels within lower and upper thresholds until 
possible image data is found. 
The film digitizer 20 with an appropriate lookup table produces twelve-bit 
values (in the range 0 to 4,095) for the optical density (OD) of each 
pixel. Presently preferred lower and upper threshold values are 500 and 
3100, respectively. These values have been conservatively chosen based on 
experience with the digitizer (a Lumisys FD 150 in one preferred 
embodiment) and film (MIN-R) used. 
Once the top of the image 44 is found (in step S50), groups of lines (eight 
lines to a group) are scanned for transition locations (step S52). This is 
done by using a sliding window 42 (FIGS. 7 and 8) (of size 1.times.50 
pixels) for each line in the group and determining whether the pixels in 
the window are image pixels. The determination is based on the relative 
number of pixels within (and outside) the threshold limits described 
above. 
Thus, for each line of the current group (or block) of eight lines, the 
window is shifted one pixel at a time and each pixel in the window is 
compared to the threshold values. If more than a certain percentage (75% 
in preferred embodiments) of the pixels are within the image thresholds, 
the window is shifted and the percentage is re-calculated. This is 
repeated until a supposed boundary is located for that line. Since the 
window is shifted one pixel at a time, it is only necessary to evaluate 
each pixel once. After the first evaluation, by keeping track of the 
values of the end pixels of the window, the percentage is easily and 
quickly recalculated. This is done by determining a total once and then 
keeping a running total as follows: add 1 for a pixel within the limits 
and subtract 1 for a pixel outside the limits. 
At the end of this process, eight left and right edge candidate pixels have 
been found, one for each line in the current block. 
Next, the left and right edges of the image for the current block (eight 
lines) are determined (step S54). This determination is made using the 
process described in FIG. 6 which assesses whether the left and right 
edges follow a trend within the image. 
First, the leftmost and rightmost boundaries of the eight lines in the 
block are selected as candidates for the left and right edges of the 
image. Then it is determined whether the edge candidates follow the 
current trend of the edge (based on previous blocks) (step S62). If the 
current edge does not follow the trend, then it is determined whether the 
trend has been consistent (step S64), and if so this edge is ignored (step 
S68) as an anomaly. That is, if the edge position has been generally 
smooth and consistent for a number of blocks, do not reset the edge 
position on widely varying transition locations. If the edge trend has not 
been consistent (such as when too few blocks have been evaluated to 
establish a trend) (step S64), then the edge position is reset to the 
current edge (step S66) and the trend is reset. 
If the transition is within the trend (step S62), then the current edge 
position is adjusted according to the current rate of change of the trend 
(step S70). That is, if the edge position is close to the last edge 
position, increase or decrease the edge position by an amount related to 
the current rate of change of the edge. 
The above process is repeated for each line in the block (step S72), at the 
end of which the edges for this block are set (step S74). 
Once the left and right edges for a block have been determined (step S54), 
extra pixels are added to the edge position (currently 150 pixels) (step 
S56) to reduce the possibility of error. 
The bounds and length of this image block are then provided to the 
compression routine (steps S58, S30) for compression. The preferred 
compression technique uses a discrete cosine transformation, quantization 
and Huffman encoding as in the extended baseline method of the JPEG 
standard. 
This process is repeated for all blocks until the entire image has been 
processed (step S60). 
Segmentation scheme 2 
The second segmentation scheme searches for a transition point by counting 
the number of very high value pixels (above a defined threshold) in a 
limited range of a line. For example, if five of ten pixels on a line have 
a digital value over 3,100. This region, in either consecutive or 
sequential lines, is then smoothed and appropriate convexity is assured. 
After several segments are done (e.g., the first fifty to five hundred 
lines of the image), the system predicts (by extending the estimated 
segmentation line) where the region of interest (suspected segmentation 
line) in the next line and performs the search for high density value 
pixels in a limited range of a line. 
Finally, the results of the two independent schemes are compared and the 
disagreements are smoothed (averaged) in order to optimize the final 
segmentation line. Differences larger than a predetermined value between 
the two schemes trigger a separate assessment of the optimal segmentation 
in that region. 
The segmented compressed image and associated information (ROIs, patient 
information etc.) are then transmitted via communications device 18 to a 
diagnosis site 14 (step S32). 
The process of on-the-fly processing above has been described in a linear, 
sequential manner However, in preferred embodiments, the digitization, 
segmentation, compression and transmission can be performed in a pipelined 
fashion as shown in the following table (wherein "D" represents 
digitization, "S" segmentation, "C" compression and "T" transmission): 
______________________________________ 
7 6 5 4 3 2 1 
______________________________________ 
D S C T 
D S C T 
D S C T 
D S C T 
______________________________________ 
Each column of this table represents a portion of the image being 
processed. The first block, no. 1, has already be digitized, segmented and 
compressed, and needs only to be transmitted. The second block, no. 2, is 
still being compressed, the third block, no. 3, is still being segmented 
and the fourth block, no. 4, is still being digitized. 
Note that each of these four processes, digitizing, segmenting and 
compression, takes a different amount of time for a particular block. 
Accordingly, the first three processes may need to store intermediate 
results while waiting for a subsequent process (in particular transmittal) 
to be ready. 
Typically the transmitter waits for the segmentation and compression 
initially, until a significant amount of data has been processed. In a 
steady state, the segmentation and compression supply data faster than the 
transmission rate and the compressed data must be stored. 
Note that a block of data sent by the transmitter has no relation to a 
block of eight lines used by the compression. 
Image quality 
The present invention uses a JPEG compression algorithm. As shown in FIG. 
10A, the JPEG algorithm operates as follows. An input image is processed 
by partitioning it into one or more sub-regions or blocks. Preferably the 
sub-regions are 8.times.8 pixel blocks. While there are still unprocessed 
image blocks, the next image block is obtained from the image (step S102) 
and the pixel values are level-shifted. The image block is then 
transformed to the frequency domain (step S104). This transform is 
performed using a linear transform such as a Fourier or Cosine transform. 
The output of such a transform is an array of frequency coefficients for 
the subregion. JPEG uses the Discrete Cosine Transform (DCT) and produces 
an 8.times.8 array of frequency coefficients for each block. The DCT 
transformed block is then quantized (step S105) using a quantization table 
45 of quantization factors. The quantization factors are intended to 
weight frequency coefficients according to their relative importance 
(according to some measure of importance). JPEG uses an 8.times.8 array of 
quantization factors, one for each frequency coefficient. During the 
quantization step (S105), the JPEG algorithm essentially divides each 
frequency coefficient by its corresponding quantization factor and then 
truncates the result to an integer value. 
After quantizing (step S105), each block is entropy encoded (step S107) to 
produce a block of compressed data. Entropy encoding consists of encoding 
the quantized frequency coefficients in a reversible manner to minimize 
the number of bits used to represent the data. JPEG uses either a form of 
Huffman encoding or a process called adaptive arithmetic encoding. 
The compressed data is uncompressed by the inverse process. First it is 
entropy decoded (step S109), after which it is de-quantized (step S111) 
using a quantization table 47 (which is usually the same as quantization 
table 45 used to quantize the block in step S105). The de-quantized block 
is then inverse transformed (IDCT) (step S113) to produce an 8.times.8 
image block. 
The 8.times.8 image blocks produced in step S113 are reassembled to form 
the decompressed image. 
The JPEG algorithm allows for modification of the quantization tables 45 
and 47. As noted above, during the quantization step (S105), the JPEG 
algorithm essentially divides each frequency coefficient by its 
corresponding quantization factor and then truncates the result to an 
integer value. This step causes loss of information during the compression 
process. The present invention determines these quantization factors (as 
described below) based on the contrast sensitivity function (CSF) for 
human vision and on the modulation transfer functions (MTF) of the 
digitizer used to produce the digitized image and on the device (e.g., a 
printer) used to output or display the image. 
Based on Human Visual System (HVS) modeling, and because different 
frequencies in the image are preferentially more or less visible to the 
human eye, the preferred quantization table is modified to match the HVS 
contrast sensitivity in such a manner that visual "degradation," (if any) 
appears equal in all frequencies of interest. Because of this optimization 
protocol, the decompressed image looks and "feels" as much as possible 
like the original image. The optimization routine depends on the type of 
image one compresses, hence, the sizes of the objects that may be of 
interest (i.e., clustered microcalcifications or masses), the digitization 
resolution (pixel size), and expected viewing distances. 
The inventors' model predicts that beyond a specific viewing distance 
(e.g., greater than fifteen cm for mammograms digitized at 50 .mu.m pixel 
size), the viewing distance has little effect on the optimal quantization 
tables in the compression scheme, as is described in more detail below. 
The JPEG compression algorithm requires that quantization tables which are 
suitable for each particular application be specified. The inventors have 
developed a unique method for generating these tables which is based on 
the contrast sensitivity function (CSF) of the human visual system as well 
as on a consideration of losses in high frequency information incurred 
during digitization and printing. The overall goal of the present 
quantization strategy is to balance the errors across frequency bands in 
such a way that the visible errors in each frequency band are 
approximately equal at all viewing distances greater than some minimum 
distance (e.g., 15 cm for 50 .mu.m pixel size). 
The quantization array required by the JPEG algorithm can be represented as 
q.sub.ij for 0.ltoreq.i, j.ltoreq.7, where the coefficient q.sub.00 is the 
DC component and q.sub.77 corresponds to the highest two-dimensional 
frequency. Each quantization value is calculated as the product of three 
factors times a constant, 
##EQU1## 
The first factor, 1/(CSF(f.sub.i, f.sub.j)), relates to the two 
dimensional contrast sensitivity function (CSF) of the human visual 
system, where frequencies f.sub.i and f.sub.j are radial frequencies, 
measured in cycles per degree as seen by an observer at a distance d (see 
FIG. 10C), and in general f.sub.k =(k+1).pi.d/2880p. The second and third 
factors correspond to the two dimensional modulation transfer functions 
(MTF) of the digitizer and printer, respectively, with g.sub.i and g.sub.j 
measured in line pairs per unit length and g.sub.k =(k+1)/16 p for pixel 
size p. The MTF of the printer may be replaced by the MTF of any other 
display device intended to be used, e.g., the MTF of the display monitor 
30. 
Because f.sub.i and f.sub.j depend on the viewing distance d, CSF(f.sub.i, 
f.sub.j) depends on d, and in general q.sub.ij will depend on d. However, 
the ratios r.sub.ij =q.sub.ij /q.sub.00 are essentially independent of d 
for distances greater than some minimum. As can be seen from the plot of 
the one-dimensional CSF (FIG. 11), the CSF is approximately linear on a 
log-log plot for frequencies greater than about 5 cycles/degree. For an 
8.times.8 pixel block, which gives the lowest frequency that can be 
directly influenced by the quantization process, this corresponds to a 
viewing distance of greater than 15 cm for an image with 50 .mu.m pixel 
size. Since f.sub.i is always (i+1) times f.sub.0, the distance between 
f.sub.0 and f.sub.i along the frequency axis is independent of d. As long 
as both f.sub.0 and f.sub.i are on the linear part of the CSF, their 
reflections s.sub.0 and s.sub.i on the sensitivity axis will be separated 
by a constant distance, implying that s.sub.0 /s.sub.i is constant. This 
can be extended to an approximation of the two-dimensional CSF to show 
that CSF(f.sub.0,f.sub.0)/CSF(f.sub.i,f.sub.j) is essentially independent 
of d for f.sub.i, f.sub.j &gt;5 cycles/degree. Thus, for sufficiently large 
d, r.sub.ij =q.sub.ij /q.sub.00 is independent of d. 
The quantization table is calculated for the CSF and MTFs by calculating a 
matrix of ratios r.sub.ij for the viewing distance corresponding to the 
linear part of the CSF curve, and then multiplying that matrix by a 
constant which has been chosen to give the desired compression ratio. 
The CSF along with the MTFs of the printer and digitizer provide all of the 
data required in order to calculate quantization tables with the desired 
properties. This optimization routine depends on the type of image one 
compresses, hence, the sizes of the objects that may be of interest (i.e., 
clustered microcalcifications or masses), the digitization resolution, 
pixel size, and expected viewing distances. However, as it turns out, our 
model predicts that beyond a specific viewing distance (e.g., fifteen 
centimeters (15 cm) for mammograms digitized at 50 .mu.m pixel size), the 
viewing distance has little effect on the optimal quantization tables in 
the compression scheme. 
Since the model predicts that most, if not all, frequencies in question are 
on the side of the sensitivity curve where linearity between log frequency 
and relative contrast sensitivity can be assumed, the quantization tables 
in the compression are set to allow for the same ratio of contrast to 
appear on the decompressed image for the frequencies in question (erg., DC 
and 20 lp/mm). 
Experiments in both breast and chest imaging have clearly demonstrated that 
a variety of quantization and processing schemes using this approach 
consistently results in decompressed images that are selected by observers 
as being visibly closest to the original, noncompressed image. 
In an experiment of the overall system using over one hundred images, the 
total cycle time (end-to-end, excluding CAD) averaged less than four 
minutes per image. 
Thus, a system for telemammography with real-time image segmentation is 
provided. One skilled in the art will appreciate that the present 
invention can be practiced by other than the described embodiments, which 
are presented for purposes of illustration and not limitation, and the 
present invention is limited only by the claims that follow.