US005999639A
Ulllted States Patent [19]
[11] Patent Number:
Rogers et al.
[45]
[54]
Date of Patent:
Dec. 7, 1999
METHOD AND SYSTEM FOR AUTOMATED
DETECTION OF CLUSTEREI)
5,627,907
5,673,332
5/1997 Gur et al. .............................. .. 382/132
9/1997 Nishikawa et al. .
382/128
MICROCALCIFICATIONS FROM DIGITAL
5,857,030
1/1999 Gaborski et al. ..................... .. 382/132
MAMMOGRAMS
[75]
5,999,639
FOREIGN PATENT DOCUMENTS
Inventors: Steven K. Rogers, Beavercreek; Philip
WO91/07135
5/1991
WIPO .................................. .. 382/128
Amburn, Dayton; Telford S. Berkey,
London; Randy P. Broussard, Huber
OTHER PUBLICATIONS
Heights; Martin P-_DeSimi0, Fairborn;
Je?'rey
Ho?'melstel', Beavercreek>
an of Ohm; Edward M‘ 0ch0a> San
Carman et al., “Detecting Calci?cations and Calci?cation
Clusters in Digitized Mammograms,” Digital Mammogra
phy ’96 (edited by Doi et al.), Elsevier Science B.V., 1996,
gntomo, TiX.,ITlhorlrE1asRP. Rathbuli,
pp 253_255, 1996_
Huber Heights, both of Ohio
Primary Examiner—AndreW W. Johns
Attorney, Agent, or Firm—Biebel & French
eavercree ;
0 n
.
osens enge ,
[73] Assignee: Qualia Computing, Inc., Beavercreek,
[57]
ABSTRACT
Ohio
[21]
Appl- NO? 09/ 141,802
[22]
Flled'
-
A method and system for detecting and displaying clustered
microcalci?cations in a digital mammogram, Wherein a
_
single digital mammogram is ?rst automatically cropped to
Aug‘ 28’ 1998
a breast area sub-image Which is then processed by means of
Related US Application Data
an optimized Difference of ('iaussians ?lter to ‘enhance the
[60] Provisional application No. 60/057,801, Aug. 28, 1997,
appearance of poFennal mlcro(_:alcl?canons 1n the Sub
provisional application No_ 60/066,996’ NOV_ 28’ 1997’ and
provisional application No. 60/076,760, Mar. 3, 1998.
[52]
[51]
image. The potential microcalci?cations are thresholded,
clusters are detected, features are computed for the detected
US.
Int. Cl?
Cl. ........................
.....................................................
.. 382/132; 382/156;
.. G06K
382/260;
9/00
.
382070
_
.
1
1th
y s oping oca
tions in the Original
382/155’ 156’ 296’ 225’ 227’ 256’ 257’
h H
res 0
b't
mg,
1
may a so
bg
e
mammogram of the Suspicious
detected clustered microcalci?cations are indicated. Param
260’ 270’ 272’ 128/922’ 706/13’ 924
eters for use in the detection and thresholding portions of the
,
system are computer-optimized by means of a genetic algo
References Clted
Us PATENT DOCUMENTS
,
,
5,463,548
y
performed by global and dual-local thresholding. The loca
Fleld Of Search ................................... ..
[56]
f p M b Y1
is pre era
rithm. The results of the system are optimally combined With
a radiologist’s observation of the original mammogram by
combining the observations With the results, after the radi
ly
golfcflbeig
/
10/1995
ta or et a . .................. ..
/
.
Asada et al. ....... ..
.. 364/413.02
""""""""""" "
ologist has ?rst accepted or rejected individual detections
5,537,485
7/1996 Nishikawa et al.
5,625,717
4/1997 Hashimoto et al. .................. .. 382/260
reported by the System‘
382/130
129 Claims, 28 Drawing Sheets
Cropped
302
Image
340
Thresholding
350
360
Remove Detections
Outside Breast
‘
Breast
Mask
Group into
370
1 Calculate
380
It Features
298
U.S. Patent
Dec. 7,1999
Sheet 1 0f28
Get Digital
5,999,639
/ 100
Mammogram
l
/ 200
Autocrop Analysis /
Region
l
300
/
Detect Clustered
\\\ Miorocaloifications <——
Optimize
Parameters
l
Classify
Microcaloi?cations ’/ 400
l
W
Process Results ’/
For Display
Display
Deteotions
Fig. 1
/
500
600
// 700
U.S. Patent
Dec. 7,1999
190
Sheet 2 0f28
200
Digital
298
Autocropping —>
Mammogram
5,999,639
Algorithm
Breast
Mask
Fig 2
190
Label
29§\
200
/
Autocropping
Algorithm
Breast
296
Background
Fig. 3
U.S. Patent
Fig. 4
Dec. 7,1999
5,999,639
Sheet 3 0f 28
Start \
202
Sub sample mammo
/
l
204
Create White Border /
t
206
Threshold Mammo /
i
208
Invert mask
//
210
Dilate Mask
,/
l
212
Crop to Largest //
Object
To Fig. 5
U.S. Patent
Dec. 7,1999
Sheet 4 0f28
5,999,639
Fig. 5
From Fig. 4
Auto Histogram
Enhance
214
/
216
Select Brighter
Side
i
218
Initialize Search /
MaskSize = 0
B = 0
To Fig. 6
U.S. Patent
Dec. 7,1999
Fig 6
Sheet 5 0f28
5,999,639
g) From Fig. 5
_
220
No
Mask Too Small?
Yes 4
l
222
Select Side to Search
/
"
224
Find Seed Pixel in
Select Side
/
/
l
Region Grow //
l
Compute Mask Size//
No
Mask Too Small?
232
At Minimum
Threshold?
V
Decrease
(75
Threshold
_
v
@
From
To
To
Fig. 7
Fig. 7
Fig. 7
U.S. Patent
Dec. 7, 1999
From
From
From
Fig 6
Fig. 6
Fig. 6
Sheet 6 of 28
5,999,639
@
236
Yes
No
Searched Both
Sides?
-
Threshold Entire
Reinitiaiize
Search
image
for other Side
238
240
/
‘
/
i
242
Close Holes
in Mask
/
/
244
V
/
Duplicate Mask
‘
246
Erode Mask
//
i
248
Dilate Mask
-
Fig. 7
/
To
Fig. 8
U.S. Patent
Dec. 7,1999
Sheet 7 0f28
5,999,639
From
Fig- 8
Fig. 7
Compute Sizes of Old
250
and New Masks
252
Is New Mask Size <
0.5 Old Mask Size?
Use Duplicate
lmage
Crop Mammogram to
256
Size of Largest Objeot/
V
258
Crop Adjustments //
Auto Histogram Enhance /
260
Cropped Mammo /
262
Apply Loose
Region Grow //
To
Fig. 9
U.S. Patent
Dec. 7,1999
Sheet 8 0f28
5,999,639
From
Fig. 9
Fig- 8
_
264
Close Holes In Mask /
i
/
266
Erode Mask
/
‘l
_
268
Dllate Mask
/
i
270
Crop Mammo to Size
of Largest Object //
V
272
Crop Adjustments //
l
Auto Histogram Enhance
274
Cropped Mammo //
i
276
Apply Tight
Region Grow
To
Fig. 10
/
U.S. Patent
Dec. 7,1999
Sheet 9 0f28
.
5,999,639
From
Flg- 10
Fig. 9
278
Find Largest Object
V
Crop Mammo to Size
of Largest Object /
280
‘
282
Cro Ad'ustments
P
J
//
284
Invert Mask
/
286
Find Largest Object//
V
_
288
Invert Largest Object
7
Close Holes in
Final Mask
End
290
//
U.S. Patent
Dec. 7, 1999
Sheet 10 0f 28
Cropped
lmage
5,999,639
302
310
Noise
Filter
l
320
Apply DOG’
Filter
i
340
/
Thresholding
i
350
Shrink to ’/
Single Pixel
360
l
Breast
Mask
Remove Detections
Outside Breast
i
370
Group into
Clusters
’
i
Calculate
380
Features '
Fig.
11
298
U.S. Patent
Dec. 7, 1999
Sheet 11 0f 28
5,999,639
P(X.y-1)
p(><-1,y)
p(><,y)
p(><,y+1)
Fig. 12
p(><+1,y)
U.S. Patent
Dec. 7, 1999
Sheet 12 0f28
5,999,639
x10-3
ovBiEm
.Y
,..
.
muzEam
-15
-10
20
xindex
Fig. 14
U.S. Patent
DoG Filtered
Image
Dec. 7, 1999
Sheet 13 0f28
p(x‘y) ‘ Compute Image
Histogram
> GlobalThresholdValue
Globally thresholded image, g(x,y):
1, p(x,y) 2 GlobalThresholdValue
O, p(x,y) < GlobalThresholdValue
Fig. 15
5,999,639
Compute Global
Threshold Value
U.S. Patent
DoG
Filtered
Dec. 7, 1999
Sheet 14 0f28
pow)
5,999,639
Select Group of
>
Pixels in N x N
lmage
Neighborhood of p(X,y)
l
Compute upper and lower
thresholds:
tlo : FLNN(XIY)+ klo 6NN(X’y)
Locally thresholded image, l(x,y):
1,
tie < p(X1y) < thi
0,
otherwise
Fig 16
U.S. Patent
Dec. 7, 1999
Sheet 15 0f28
Fig. 17
5,999,639
U.S. Patent
Dec. 7, 1999
Sheet 16 0f28
Center (N X N)
Digital
Mammogram
5,999,639
Compute mean and
window Over _>
piXe| pow)
standard deviation of
pixels under the window
MW) and 609v)
V
gig
Compute local threshold:
T(X.y) = A + B- ulky) + 0- 6(X,y)
d(X,y)
‘
Local Threshold
d(Xry) > T(X,y)
v
Locally thresholded image, l(x,y):
Fig. 18
U.S. Patent
Dec. 7, 1999
(x,y) coordinates
5,999,639
Sheet 17 0f 28
Compute Distance Matrix
of centroid
locations
D =[ d(i,i)]
V
Identify and count points
Eliminate points with fewer
within distance
than uCsmin
neighbors within dNN
dNN
of each other
V
Merge clusters
Lists of points
sharing one or
associated with
more common
each remaining
points
cluster
Fig. 19
U.S. Patent
Dec. 7, 1999
Sheet 18 0f28
5,999,639
10
$296:1
94037625
.:
0
1O
Column Index
Fig. 20
U.S. Patent
Dec. 7, 1999
Sheet 19 0f 28
5,999,639
Points Associated
With Clusters
+
Compute lnterpoint
Compute Covariance
Distance Matrix
D
Matrix of Points in Cluster
Compute Eigenvalues
of Covariance Matrix
9\’1
‘
Number Points in Cluster
)LZ
Rectangular Area
_, &2
x1
_>
Standard
‘
Deviation(D)
V
—-w
Mean(D) - Median(D)
+
~M
Max(D) - Min(D)
>
Number Points in Cluster
Max(D)
Fig. 21