automated evaluation of radiodensities in a digitized mammogram database using local contrast...
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Automated evaluation of Automated evaluation of radiodensities in a digitized radiodensities in a digitized
mammogram database using local mammogram database using local contrast estimationcontrast estimation
Automated evaluation of Automated evaluation of radiodensities in a digitized radiodensities in a digitized
mammogram database using local mammogram database using local contrast estimationcontrast estimationThesis Advisor: Dr. Mandayam
Committee: Dr. Kadlowec and Dr. Polikar
Friday, July 23, 2004
OutlineOutline
• Introduction
• Objectives of the Thesis
• Previous Work
• Approach
• Results
• Conclusions
Cancer Related Deaths in the U.S. (women)
Urinary3.22%
Skin1.33% Soft Tissue
0.70%
Bones0.22%
Respiratory System25.98%
Digestive System22.80%
Breast14.71%
Genital9.90%
Oral/Phalanx0.89%
Other7.61%
Leukemia3.62%
Mulitple Myeloma2.03%
Lymphoma4.36%
Eye0.04%
Endocrine0.44%
Brain2.14%
IntroductionIntroduction
Breast CancerBreast CancerNew Cases of Cancer in the
U.S. (women)
Breast32.07%
Genital12.70%
Digestive System18.23%
Skin4.02%
Soft Tissue0.58%
Respiratory System12.69%
Bones0.17%
Oral/Phalanx1.44%
Other2.46%
Leukemia1.93%
Mulitple Myeloma1.03%
Urinary4.31%
Eye0.17%
Brain1.23%
Endocrine2.61%
Lymphoma4.36%
Survival RatesSurvival Rates
0 100%
I 98%
IIA 88%
IIB 76%
IIIA 56%
IIIB 49%
IV 16%
Each stage designates the size of the tumor how much it has spread.
Stage 0 Cancer:
Lobular Carcinoma in Situ (LCIS)
Ductal Carcinoma in Situ (DCIS)
20% of all diagnosed cancers
Mammography ProcedureMammography Procedure
Compression Plate
Compression Plate
Film Holder
Pectoral Muscle
Film Holder
MLOView
CCView
Risk Factor High-Risk Group Low-Risk Group Relative risk
Age Old Young > 4.0
Country of birth North America, Northern Europe
Asia, Africa > 4.0
Socioeconomic status High Low 2.0 – 4.0
Marital Status Never married Ever married 1.1 – 1.9
Place of residence Urban Rural 1.1-1.9
Place of residence Northern US Southern US 1.1-1.9
Race ≥ 45 years < 40 years
WhiteBlack
BlackWhite
1.1-1.91.1-1.9
Nulliparity Yes No 1.1-1.9
Age at first full-term pregnancy ≥ 30 years < 20 years 2.0-4.0
Age at menopause Late Early 1.1-1.9
Weight, postmenopausal women Heavy Thin 1.1-1.9
Any first-degree relative with history of breast cancer
Yes No 2.0-4.0
Mother and sister with history of breast cancer
Yes No > 4.0
Mammographic parenchymal patterns
Dysplastic Normal 4.0-6.0
Risk FactorsRisk Factors
Breast densityBreast density
Chest wall
Pectoralis muscles
Lobules
Nipple surface
Areola
Duct
Fatty tissue
Skin
Radiodense
Tissue
Radiolucent
Tissue
RadiodensityRadiodensity
Chest wall
Pectoralis muscles
Lobules
Nipple surface
Areola
Duct
Fatty tissue
Skin
Radiodense
Tissue
Radiolucent
Tissue
Mammographic DensityMammographic Density
“……..women who had a breast density of 75% or greater had an almost fivefold increased risk of breast cancer…………”
– Byrne, C, et. al. “Mammographic features and breast cancer risk: effects with time, age, and menopause status,” Journal of the National Cancer Institute, Vol. 87, pp.1622-1629, 1995.
Genetic HeritabilityGenetic Heritability“Women with extensive dense breast tissue visible on
mammogram have a risk of breast cancer that is 1.8 to 6.0 times that of women of the same age with little or no density.”
“…………….. the percentage of dense tissue on mammography at a given age has high heritability. Because mammographic density is associated with an increased risk of breast cancer, finding the genes responsible for this phenotype could be important for understanding the causes of the disease.”
– Boyd, N.F., et al, “Heritability of mammographic density, a risk factor for breast cancer,” New England Journal of Medicine, Volume 347(12), September 19, 2002, pp. 886-894.
IssuesIssues
• Current methods are still slow and subjective.
• Variability still exists between radiologists.
• Automated algorithm for fast and objective estimation.
• Rowan University.
Objectives of this ThesisObjectives of this Thesis
• Investigate the use of textures for the segmentation of radiodense tissue in a digitized mammogram.
• Create an automated algorithm that is able to consistently evaluate digitized mammograms throughout several databases.
• Compare the results of the algorithm to a established manual methods, the “Toronto” method as well as previous methods created at Rowan University.
TexturesTextures
Region of Mammogram
Texture Description Method
Feature 1
Feature 2
Feature 3
Feature …
Un-supervised Clustering Method
Mammogram
Total Bank of Features
Classified Mammogram
f1 f2 f3 … fn
f1(1,1) f1(1,2) f1(1,3) …f1(1,j)
f1(2,1) f1(2,2) f1(2,3) …f1(2,j)
f1(3,1) f1(3,2) f1(3,3) …f1(3,j)
…f1(i,1) …f1(i,2) …f1(i,3) …f1(i,j) …
Automated AlgorithmAutomated Algorithm
Database 1
Estimated percentages for database 1
Automated Process
Database 2
Estimated percentages for database 2
Validation PercentagesCompare Compare
Performance on database 1
Performance on database 2
PERFORMANCES ‘”SHOULD” BE
SIMILAR
Previous WorkPrevious Work
• Wolfe’s classification.
• “Toronto” method.
• Automated techniques.– “Main goal of research conducted at
Rowan University”
Wolfe’s ClassificationWolfe’s Classification
• N1: The breast is comprised entirely of fat.
• P1: The breast has up to 25% nodular densities.
• P2: The breast has over 25% nodular mammographic densities.
• DY: The breast contains extensive regions of homogeneous mammographic densities.
““Toronto” MethodToronto” Method
Display Results
33.3% RD
66.6% RL
Load Image into Computer
Set Boundary Threshold
1 4096
Set TissueThreshold
1 4096
Count pixels inregions
1 4096
Display Results
33.3% RD
66.6% RL
Load Image into Computer
Set Boundary Threshold
1 4096
Set TissueThreshold
1 4096
Count pixels inregions
1 4096
AutomatedAutomated
Proponents Approach Advantages Disadvantages
Lou and Fan [35] Adaptive fuzzy K-means technique to classify pixels as radiodense.
7.98 % error among 81 mammogram images.
18 seconds process time per image.
Zou et al. [36,37] Rule based histogram classifier
Maximum difference 20% from expert analysis.
No objective method for validation.
Bovis and Singh [38]
Classification using texture analysis.
91 % correct classification. Relies on knowledge of the region to be segmented.
Classifier is based on simplistic measures of texture.
Saha, Udupa, et al. [39]
Scale-based fuzzy connectedness
models
Estimates correlate strongly with analysis by radiologist.
Does not automatically exclude pectoral muscle.
Neyhart et al. [40]Eckert et al.
[41]
Constrained Neyman-Pearson decision
functionw/wo
Compression Adjustment
Automated technique Performance fit to database tested with. Weak inter-
dataset performance.
Neyman-Pearson ClassifierNeyman-Pearson Classifier
Distribution 1(Radiolucent)
Distribution 2(Radiodense)
12, 21=2
2
12
212
2
NPT
1 2
TNP1 2
TNP
Gray-level intensity
Num
ber
of P
ixel
s
1 and 2 are means of distributions, 2 is local variance of image
• Varies threshold based on the variance of image from pure Bayesian to 2
• Can compensate for brightness of image and classify image radiodensity
• Determine from training data set
Constrained Neyman-Pearson Constrained Neyman-Pearson ClassifierClassifier
2212
221
CNPT
Spatially Varying CNPSpatially Varying CNP
Compression Plate
Film Holder
CCView
More StressHere
Less StressHere
More DensityHere
Less DensityHere
Compression CompensationCompression Compensation
Multiple lowpass filtering operations
IssuesIssues
• Most of these algorithms are not fully automated.
• Performance is evaluated in just one type of database.
ApproachApproach
• Texture and image processing methods investigated.
• Local Contrast Estimation algorithm.
• Investigation of previous methods created at Rowan University.
TexturesTextures
• If radiodense and radiolucent tissue exhibit characteristics that are different from each other, texture…
• Evaluation of 3 different ‘types’ of methods.
Texture description methods
VarianceVariance
10 images:
5 FCC
5 Harvard
Database Evaluation
Individual Variance Imaging
Intra-image characteristics
evaluation
Inter-image and cross-database
statistic evaluation
Variability of texture characteristics
Variance ImagingVariance Imaging
Regional Variance Imaging
Histogram
Histogram
Gabor FilteringGabor Filtering
})([2exp{})([2exp{),( 220
22220
22 vuuvuuvuH yxyx )2cos(2
1exp
2
1),( 02
2
2
2
xyx
yxhyxyx
Spatial Domain Frequency Domain
Gabor FilteringGabor Filtering
50 regionalsamples forradiodensetissue
50 frequencyprofiles forradiodensetissue
2-Dimensional FFT
2-Dimensional FFT
50 regionalsamples forradiolucenttissue
50 frequencyprofiles forradiolucenttissue
averagedfrequencyprofile for radiodensetissue
averagedfrequencyprofile for radiolucenttissue
highestregion ofdifference
Co-occurrenceCo-occurrence
|}),(,),(,||,0:)],(),,{[(|),(0 bnmfalkfdnlmkDnmlkbaP
0 0 1 10 0 1 10 2 2 22 2 3 3
4 2 1 02 4 0 01 0 6 10 0 1 2
),(0 baP
Co-occurrenceCo-occurrence
ba
d baP,
2, ),(
)),((log),( ,,
2, baPbaP dba
d
ba
dk baPba
,, ),(||
baba
k
d
ba
baP
,,
,
||
),(
Energy
Moments
Entropy
Inverse Moments
Law’s Texture Energy MeasuresLaw’s Texture Energy Measures
• Spatial filters based on three simple vectors:– Averaging L = (1,2,1)– Edges E = (-1,0,1)– Spots S = (-1,2,1)
• These 3 vectors can be combined to make 25 separate spatial filters.
Law’s Texture FilterLaw’s Texture FilterL5 = [ 1 4 6 4 1 ] E5 = [-1 -2 0 2 1 ] S5 = [-1 0 2 0 -1 ] W5 = [-1 2 0 -2 1 ] R5 = [ 1 -4 6 -4 1 ]
1 4 6 4 1 4 16 24 16 4 6 24 36 24 6 4 16 24 16 4
1 4 6 4 1
1
4
6
4
1
[ 1 4 6 4 1 ]
3 Simple Vectors
All convolution pairs
5 Vectors
All column by row multiplication pairs
25 matrices (filters)Filtering +energy
measure + addition of Complements
Set of 15 features
Image
Averaging L = (1,2,1)Edges E = (-1,0,1)Spots S = (-1,2,1)
[1 2 1]*[1 2 1] = [1 4 6 4 1]
x =
25 filtersFiltering Result Energy measuring function
Complements are summed
15 sets of features
ClusteringClustering
• Variance Imaging = 1 feature.
• Co-occurrence = 4 features.
• Law’s Energy Measure = 15 features.
Supervised Learning Techniques are not viable because of the vast variation texture characteristics!!!
k-meansk-meansbegin initialize n, c, µ1, µ2 … µc
do classify n samples according to nearest µi
recompute µi
until no change in µi
return µ1, µ2 … µc
end
Image Processing Techniques for Image Processing Techniques for pre-processing & evaluationpre-processing & evaluation
• Non-linear Transformations.
• Gray level connectivity.
Non-Linear TransformationNon-Linear Transformation
xI =
2 3 2 4 3 5 6 6 5 4
4xI
=
16 81 16 256 81 625 1296 1296 625 625
Gray Level Connectiveness Gray Level Connectiveness
Both 50% of dark pixels and 50% bright pixels
Gray Level ConnectivenessGray Level Connectiveness
• Classify the lowest set of pixels .1 gray values away from each other as radiodense.
• Afterwards, all regions were analyzed for connectiveness by classifying regions as connected as long as they were within .1 gray values of each other.
VarianceVariance
Typical Harvard
Typical FCCC
VarianceVariance
Variance of Radiodense Regions
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
1159
9502
1448
0101
1913
1709
2625
3102
2865
7701
0691
7201
0691
7201
2227
2101
2276
5901
0308
3801
Var
ian
ce
VarianceVariance
Variance of Radiolucent Regions
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
1159
9502
1448
0101
1913
1709
2625
3102
2865
7701
0691
7201
0691
7201
2227
2101
2276
5901
0308
3801
Var
ian
ce
Gabor FiltersGabor Filters
Typical Harvard
Typical FCCC
Co-occurrence MatrixCo-occurrence Matrix
Results of clustering
Expected result
Co-occurrence MatrixCo-occurrence Matrix
Results of clustering
Expected result
Law’s EnergyLaw’s Energy
Non-linear TransformationNon-linear Transformation
Non-linear TransformationNon-linear Transformation
ConnectivenessConnectiveness
Texture Conclusions Texture Conclusions
• Characteristics of radiodense and radiolucent tissue vary from image to image as well as region to region.
• The two regions have similar characteristics. So similar that separability seems unlikely.
Local Contrast Estimation (LCE)Local Contrast Estimation (LCE)
• Image Preprocessing• Tissue Segmentation
Mask• Compensation for
Compression• Threshold Selection
based on Local Contrast Estimation.
Tissue Segmentation
Compression Mask
Threshold Based on Global Estimate of Range
Image Compression Adjustment
Image Pre-Processing
Radiodensity Estimation of Mammogram
Image PreprocessingImage Preprocessing
Stripes caused during mammogram scan
Image ProcessingImage Processing
50 Percent of Left Side
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Mean.3715
1.5 SigmaThreshold
Statistical analysis
Binary Segmentation
Stripe Removal
Tissue SegmentationTissue Segmentation
• SV-CNP – Bimodal Histogram Analysis through block processing and RBF generalization.
• LCE- Image Morphology and RBF generalization.
Tissue SegmentationTissue Segmentation
(a) (d)(c)(b)
Previous method of tissue segmentation
Tissue SegmentationTissue SegmentationPrevious method of tissue segmentation
Tissue SegmentationTissue SegmentationPrevious method of tissue segmentation
Harvard FCCC
Tissue SegmentationTissue SegmentationPrevious method of tissue segmentation
The white stripes were sometimes brighter than the tissue itself
Tissue Segmentation Tissue Segmentation (Morphology)(Morphology)
Tissue Segmentation Tissue Segmentation (Generalization)(Generalization)
• Obtain enhanced image.• Group averaging for generalization.• Obtain initial semicircle RBF mask.• Starting from center, check pixel by pixel for
mean square error.• Only include pixels that have Euclidean distance
MSE below threshold for next iteration of mask.• Implement until no changes are made.
Tissue Segmentation Tissue Segmentation (Generalization)(Generalization)
Tissue Segmentation Tissue Segmentation (Generalization)(Generalization)
Compensation for CompressionCompensation for Compression
• SV-CNP – Homotopy Continuation algorithm.– Multiple filtering operations.
• LCE– Gaussian interpolation.
Compensation for Tissue Compensation for Tissue Compression (SV-CNP)Compression (SV-CNP)
From mask, obtain boundaries
From, boundaries, obtain family of curves
Multiple filter operations to obtain final image
Compensation for Tissue Compensation for Tissue Compression (SV-CNP)Compression (SV-CNP)
Scale problem
Resolution problem
Resolution problem
Compensation for Tissue Compensation for Tissue Compression (SV-CNP)Compression (SV-CNP)
Region A
Region B
Compensation for Tissue Compensation for Tissue Compression (LCE)Compression (LCE)
Line by line interpolation of a Gaussian function
Local Contrast Estimation (LCE)Local Contrast Estimation (LCE)
• Perceive connected regions a layers.
Boundary EstimationBoundary Estimation
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
500
1000
1500
2000
2500
grey level values
occu
renc
e
From the mask, the locations of these
artificial boundaries created by threshold
t is then found
Local Contrast EstimationLocal Contrast Estimation
Using threshold t, the mask of the
radiodense regions is created
Step 1: Estimation of the Boundaries
Local Contrast EstimationLocal Contrast EstimationStep 1: Boundary Estimation
Any white pixel in the new boundary mask will correspond a region where the estimated threshold t believes there is a change from radiolucent to radiodense tissue.
Local Contrast EstimationLocal Contrast EstimationStep 2: Calculation of Local Contrast
From this region selected by the boundary mask, a collection of pixels is collected by four methods:
Horizontal
Vertical
Southwest-Northeast Diagonal
Southwest-Northeast Diagonal
Split into two groups,
7 numbers higher than median
7 number lower than median
The highest function of 4 methods
= local Edge Function
Local Contrast EstimationLocal Contrast EstimationStep 2: Calculation of Local Contrast
For each collection method:
Horizontal Vertical Southwest-Northeast Diagonal
Southwest-Northeast Diagonal
: a local contrast estimation is calculated
Calculate median
Find Difference of two group means
MH - ML
Local Contrast EstimationLocal Contrast EstimationStep 3: Calculation of Global Contrast
After the local contrast estimation is obtained for all regions defined by the mask, and average global estimate is obtained.
N
iContrastContrast
N
iLocal
Global
1
)(
)()()(
))(max(
LowH
Local
GroupmeanGroupmeaniContrast
iContrastContrast
where i is one of four collection methods
N being the total number of Local Contrasts
• A sweep of thresholds is done for each image.
0.46 0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64 0.660.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
Local Contrast EstimationLocal Contrast EstimationStep 4: Calculation of Optimum Contrast
Glo
bal
Con
tras
t
Threshold
(Only small region is being shown in graph)
Based on graph, the threshold with the highest global contrast is chosen as the optimum threshold
ResultsResults
• Databases.
• Scanners.
• LCE
• LCE vs. CNP vs. SV-CNP.
DataDataBrigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Brigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Fox Chase Cancer CenterPhiladelphia, PAFox Chase Cancer CenterPhiladelphia, PA
Rowan UniversityGlassboro, NJRowan UniversityGlassboro, NJ
Digitized bmpImages (10)
Mammogram X-Ray Films
Digitized jpg & dicomImages (717)
FRAP (339) Chinese-American (378)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
Scanner ComparisonsScanner Comparisons
AGFA Scanner Lumisys Scanner
Local Contrast EstimationLocal Contrast Estimation
• Image Preprocessing• Tissue Segmentation
Mask• Compensation for
Compression• Threshold Selection
based on Local Contrast Estimation.
Tissue Segmentation
Compression Mask
Threshold Based on Global Estimate of Range
Image Compression Adjustment
Image Pre-Processing
Radiodensity Estimation of Mammogram
Image PreprocessingImage Preprocessing
Tissue Mask SegmentationTissue Mask Segmentation
Tissue Mask SegmentationTissue Mask Segmentation
Compression Compensation MaskCompression Compensation Mask
Comparison between 3 methodsComparison between 3 methods
Problems with CNPProblems with CNP
2212
221
CNPT
Supervised Parameter
Problems with SV-CNPProblems with SV-CNP
• Based on threshold from CNP….
• Compression values over fit data to correlate with percentages.
• Final segmentation results do not visually match with the expected segmentation.
Problems with SV-CNPProblems with SV-CNP
Problems with SV-CNPProblems with SV-CNP
DataDataBrigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Brigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Fox Chase Cancer CenterPhiladelphia, PAFox Chase Cancer CenterPhiladelphia, PA
Rowan UniversityGlassboro, NJRowan UniversityGlassboro, NJ
Digitized bmpImages (10)
Mammogram X-Ray Films
Digitized jpg & dicomImages (717)
FRAP (339) Chinese-American (378)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
Harvard ResultsHarvard Results
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
11051702 11599502 14480101 15839502 19131709 20110811 26253102 26799401 2778620 28657701
TorontoCNPSV-CNPLCE
Harvard ResultsHarvard Results
91% 1091
92% 495
87% 879
CNP
SV-CNP
LCE
MSECorrelation
DataDataBrigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Brigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Fox Chase Cancer CenterPhiladelphia, PAFox Chase Cancer CenterPhiladelphia, PA
Rowan UniversityGlassboro, NJRowan UniversityGlassboro, NJ
Digitized bmpImages (10)
Mammogram X-Ray Films
Digitized jpg & dicomImages (717)
FRAP (339) Chinese-American (378)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
34 selected validation images
CNP FCCCCNP FCCC
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0Toronto
CNP
SV-CNP FCCCSV-CNP FCCC
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0Toronto
SV-CNP
LCE FCCCLCE FCCC
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
Toronto LCE
FCCC ResultsFCCC Results
-39% 21732
48% 15127
84% 4052
CNP
SV-CNP
LCE
MSECorrelation
DataDataBrigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Brigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Fox Chase Cancer CenterPhiladelphia, PAFox Chase Cancer CenterPhiladelphia, PA
Rowan UniversityGlassboro, NJRowan UniversityGlassboro, NJ
Digitized bmpImages (10)
Mammogram X-Ray Films
Digitized jpg & dicomImages (717)
FRAP (339) Chinese-American (378)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
34 selected validation images
Combined ResultsCombined Results CNP SV-CNP LCE
Correlation compared to Toronto method (with
flagged) 0.147 0.565 0.855
Correlation compared to Toronto method (without flagged) 0.306 0.733 0.882
MSE compared to Toronto method (with
flagged) 22823.930 15622.682 4931.374
MSE compared to Toronto method (without flagged) 14381.540 5425.792 2811.690
Average % difference compared to Toronto method (with flagged) 18.186 12.924 8.232
Average % difference compared to Toronto
method (without flagged) 17.889 8.791 7.927
DataDataBrigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Brigham and Women’s HospitalHarvard Medical SchoolCambridge, MA
Fox Chase Cancer CenterPhiladelphia, PAFox Chase Cancer CenterPhiladelphia, PA
Rowan UniversityGlassboro, NJRowan UniversityGlassboro, NJ
Digitized bmpImages (10)
Mammogram X-Ray Films
Digitized jpg & dicomImages (717)
FRAP (339) Chinese-American (378)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
Digitizers:Agfa (OD = 3.0)Lumisys (OD = 3.85)
Database ResultsDatabase Results
182 27.5%
133 20%
87 13%
73 11%
50 7.5%
32 4.8%
10 1.5%
0 0%
0 0%
0 0%
Out of 660 images
0%-10%
10%-20%
20%-30%
30%-40%
40%-50%
50%-60%
60%-70%
70%-80%
80%-90%
90%-100%
# images % of database
14% of the images could not be evaluated
Database ResultsDatabase Results
37.9% 23.9%
19.5% 19.5%
9.8% 14%
10% 11%
5.7% 9.9%
1.4% 3.3%
.3% 1.5%
0% 0%
0% 0%
0% 0%
0%-10%
10%-20%
20%-30%
30%-40%
40%-50%
50%-60%
60%-70%
70%-80%
80%-90%
90%-100%
FCCC Chinese American
Database IssuesDatabase Issues
• 93 images could not be analyzed.
Summary of AccomplishmentsSummary of Accomplishments
• Development of a comprehensive database from multiple age and ethnic groups.
• Development of a completely automated radiodense tissue segmentation procedure.
• Comparison of new method with a previously established segmentation method.
• Algorithm has the ability to sift through entire databases of digitized mammograms quickly.
ConclusionsConclusions
• LCE is able to give good performances across multiple databases without the need to supervise.
• LCE is fully automated.• LCE is 86% correlated with an established
method• The average difference in percentage is less
than 8.3%
IssuesIssues
• Tissue segmentation algorithm still has trouble generating accurate boundaries for low contrast images.
• For images that had multiple layers of gray level intensities, the algorithm has no clue which layer is supposed to be chosen to achieve the estimate comparable to the radiologist’s.
Recommendations for Future Recommendations for Future WorkWork
• Canonical images for radiodensity segmentation.
• Accurate model for tissue compression.
• Mammograms from the scanned using the Agfa should be scanned again.
AcknowledgementsAcknowledgements
• Dr. Marilyn Tseng, FCCC
• Dr. Celia Byrne
• Dan Barrot
• Lyndsay Burd
• Any questions before I get married………???