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S. Mandayam, ECE Dept., Rowan University
Automated Segmentation of Automated Segmentation of Radiodense Tissue in Radiodense Tissue in
Digitized MammogramsDigitized Mammograms
Automated Segmentation of Automated Segmentation of Radiodense Tissue in Radiodense Tissue in
Digitized MammogramsDigitized Mammograms
Department of Electrical & Computer Engineering
201 Mullica Hill RoadGlassboro, NJ 08028, USA
engineering.rowan.edu
Shreekanth MandayamShreekanth Mandayam
GE John F. Welch Technology CenterBangalore, India
July 15, 2005
S. Mandayam, ECE Dept., Rowan University
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%
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
Digitized MammogramDigitized Mammogram
Radiolucent
RadiodenseFilm region
S. Mandayam, ECE Dept., Rowan University
Breast Density and Breast Cancer RiskBreast Density and Breast Cancer Risk
“……..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.
S. Mandayam, ECE Dept., Rowan University
Breast Density and Breast Cancer RiskBreast Density and Breast Cancer Risk
“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.
S. Mandayam, ECE Dept., Rowan University
Collaboration with Fox Chase Collaboration with Fox Chase Cancer Center, Phila.Cancer Center, Phila.
• Correlation between diet and breast cancer risk
• Two populations– FRAP (Family Risk Analysis Program):
Caucasian– Chinese-American: Three Generations
(Grandmothers, mothers, daughters)
S. Mandayam, ECE Dept., Rowan University
TeamTeam
• Rowan University– Dr. Shreekanth Mandayam, Jeremy Neyhart, Rick
Eckert, Mike Kim, Maggie Kirlakovsky, Laura Coleman, Lyndsay Burd, Dan Barrot, Kevin Kanauss
• Fox Chase Cancer Center, Philadelphia– Dr. Marilyn Tseng, Dr. Kathy Evers MD
• Harvard Medical School/George Washington– Dr. Celia Byrne
S. Mandayam, ECE Dept., Rowan University
Previous WorkPrevious Work
• Wolfe’s classification.
• “Toronto” method.
• Automated techniques.– “Main goal of research conducted at
Rowan University”
S. Mandayam, ECE Dept., 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.
S. Mandayam, ECE Dept., Rowan University
““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
S. Mandayam, ECE Dept., Rowan University
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.
S. Mandayam, ECE Dept., Rowan University
Limitations of Limitations of Previous MethodsPrevious Methods
• Highly qualitative and subjective• Requires user (radiologist’s) interaction• Do not provide relationship to actual breast
density measurements• Requires knowledge of region of interest
• No completely automated system exists – requires expert user interaction
S. Mandayam, ECE Dept., Rowan University
Low Radio-density, Dark Image
High Radio-density, Dark Image
High Radio-density, Bright Image
Low Radio-density, Bright Image
Statement of the ProblemStatement of the Problem
Cannot use a single threshold for every image!!!!Radiodensity is not objectively defined……….
S. Mandayam, ECE Dept., Rowan University
Mammography ProcedureMammography Procedure
Compression Plate
Compression Plate
Film Holder
Pectoral Muscle
Film Holder
MLOView
CCView
S. Mandayam, ECE Dept., Rowan University
Overall Approach: FlowOverall Approach: Flow
Threshold Radiodense tissue quantified
X =
Original Image Mask Segmented Tissue
1 0
Gray-level
Dec
isio
n
Radiolucent
Radiodense
S. Mandayam, ECE Dept., Rowan University
Image Model: Image Model: Gaussian Random FieldGaussian Random Field
),(),( yxwmyxf ff
Original Modeled
S. Mandayam, ECE Dept., Rowan University
Baye’s ClassifierBaye’s Classifier
TB
Gray-level intensity
Num
ber
of P
ixel
s
Distribution 1(Radiolucent)
Distribution 2(Radiodense)
12, 21=2
2
221 BT
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
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)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)
S. Mandayam, ECE Dept., Rowan University
Constrained Neyman-PearsonConstrained Neyman-Pearson
TCNP
Gray-level intensity
Num
ber
of P
ixel
s
Distribution 1(Radiolucent)
Distribution 2(Radiodense)
12, 21=2
2
2212
221
CNPT
S. Mandayam, ECE Dept., Rowan University
Test Images: Harvard-10Test Images: Harvard-10
19131709 20110811 11599502 15839502 11051702
26253102 28657701 14480101 27786202 26799401
S. Mandayam, ECE Dept., Rowan University
CNP Results: Harvard-10CNP Results: Harvard-10
S. Mandayam, ECE Dept., Rowan University
CNP Results: Harvard-10CNP Results: Harvard-10
0
10
20
30
40
50
60
70
1913
170
2011
081
1159
950
1583
950
1105
170
2625
310
2865
770
1448
010
2778
620
2679
940
Image Number
Per
cen
t R
adio
den
sity
"Toronto" Method Percentage Radiodensity
Constrained Neyman Pearson Percentage Radiodensity
S. Mandayam, ECE Dept., Rowan University
Effect of Tissue CompressionEffect of Tissue Compression
Compression Plate
Film Holder
CCView
More StressHere
Less StressHere
More DensityHere
Less DensityHere
S. Mandayam, ECE Dept., Rowan University
Parametric Model for Parametric Model for Tissue Location & CompressionTissue Location & Compression
2
2
2)( x
CNPv ekTxT
x
chestedgechestt ffNt
fy
S. Mandayam, ECE Dept., Rowan University
Test Images: Harvard-10Test Images: Harvard-10
19131709 20110811 11599502 15839502 11051702
26253102 28657701 14480101 27786202 26799401
S. Mandayam, ECE Dept., Rowan University
CNP-SV Results: Harvard-10CNP-SV Results: Harvard-10
19131709 20110811 11599502 15839502 11051702
26253102 28657701 14480101 27786202 26799401
S. Mandayam, ECE Dept., Rowan University
CNP, CNP-SV and Toronto CNP, CNP-SV and Toronto Comparisons: Harvard-10Comparisons: Harvard-10
0
10
20
30
40
50
60
70
8019
1317
09
2011
0811
1159
9502
1583
9502
1105
1702
2625
3102
2865
7701
1448
0101
2778
6202
2679
9401
Image number
Per
cen
tag
e ra
dio
den
se t
issu
e
Percentage radiodense using Toronto method
Percentage radiodense using CNPA
Percentage radiodense using SVTA
S. Mandayam, ECE Dept., Rowan University
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)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)
S. Mandayam, ECE Dept., Rowan University
Methods StudiedMethods Studied
• Textures– Correlation Filters– Co-occurrence Matrix– Gabor Filters– Law’s Energy
S. Mandayam, ECE Dept., Rowan University
Local Contrast EstimationLocal Contrast Estimation
• Maximize the local contrast between boundaries of connected tissue regions
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
Image PreprocessingImage Preprocessing
Stripes caused during mammogram scan
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
Local Contrast Estimation (LCE)Local Contrast Estimation (LCE)
• Perceive connected regions a layers.
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
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.
S. Mandayam, ECE Dept., Rowan University
Local Contrast EstimationLocal Contrast EstimationStep 2: Calculation of Local Contrast
From this region selected by the boundary mask, a collection of pixels is gathered.
S. Mandayam, ECE Dept., Rowan University
Split into two groups,
7 numbers higher than median
7 number lower than median
= local Edge Function
Local Contrast EstimationLocal Contrast EstimationStep 2: Calculation of Local Contrast
Calculate median
Find Difference of two group means
MH - ML
S. Mandayam, ECE Dept., Rowan University
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
N being the total number of Local Contrasts
S. Mandayam, ECE Dept., Rowan University
• 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
S. Mandayam, ECE Dept., Rowan University
ResultsResults
• Databases.
• Scanners.
• LCE
• LCE vs. CNP vs. SV-CNP.
S. Mandayam, ECE Dept., Rowan University
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)
S. Mandayam, ECE Dept., Rowan University
Scanner ComparisonsScanner Comparisons
AGFA Scanner Lumisys Scanner
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
Image PreprocessingImage Preprocessing
S. Mandayam, ECE Dept., Rowan University
Tissue Mask SegmentationTissue Mask Segmentation
S. Mandayam, ECE Dept., Rowan University
Tissue Mask SegmentationTissue Mask Segmentation
S. Mandayam, ECE Dept., Rowan University
Compression Compensation MaskCompression Compensation Mask
S. Mandayam, ECE Dept., Rowan University
Comparison between 3 methodsComparison between 3 methods
S. Mandayam, ECE Dept., Rowan University
Problems with CNPProblems with CNP
2212
221
CNPT
Supervised Parameter
S. Mandayam, ECE Dept., Rowan University
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.
S. Mandayam, ECE Dept., Rowan University
Problems with SV-CNPProblems with SV-CNP
S. Mandayam, ECE Dept., Rowan University
Problems with SV-CNPProblems with SV-CNP
S. Mandayam, ECE Dept., Rowan University
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)
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
Harvard ResultsHarvard Results
91% 1091
92% 495
87% 879
CNP
SV-CNP
LCE
MSECorrelation
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
CNP FCCCCNP FCCC
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0Toronto
CNP
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
FCCC ResultsFCCC Results
-39% 21732
48% 15127
85% 4052
CNP
SV-CNP
LCE
MSECorrelation
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
Combined ResultsCombined Results CNP SV-CNP LCE
Correlation compared to Toronto method (with
flagged) 0.147 0.565 0.851
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
S. Mandayam, ECE Dept., Rowan University
AnalysesAnalyses
• Correlation (Pearson r)– To determine if there is a significant relationship
between the LCE and Toronto Methods• Results revealed a r (30) = .851, p = .001.
• This means: The correlation between LCE and Toronto is .851, then 72.5% of the differences between LCE in terms of the relationship is predictable on the basis of the Toronto Method.
• 27.5% is not predictable, due to error.
S. Mandayam, ECE Dept., Rowan University
AnalysesAnalyses
• Linear Regression– Since there was such a high correlation and strong
significance found, ran a linear regression to confirm the conclusions of the correlation and develop a regression equation.
• 1 Independent Variable (Predictor)-LCE• 1 Dependent Variable (Predicted)-Toronto
– Results yielded that LCE scores are significant predictors to the Toronto method
• Y = -7.523 E -3 + (1.030)(LCE Method)• F (1, 30) = 78.834, p = .001• Effect size, ^2 = .725 (from correlation).
S. Mandayam, ECE Dept., Rowan University
Linear RelationshipLinear Relationship
Scattergram of LCE and Toronto Method
0
0.2
0.4
0.6
0.8
0 0.2 0.4 0.6 0.8 1
Toronto Method (Dr. Byrne)
Lo
cal C
on
trast
Esti
mate
s (
LC
E)
S. Mandayam, ECE Dept., Rowan University
Analysis SummaryAnalysis Summary
• Linear Regression exhibits and verifies that the LCE method can predict what a radiologist would detect to a significant degree (73% accurate).
• There will always be some variance explained by error (the image was poor, the position of the breast was incorrect, etc.).
S. Mandayam, ECE Dept., Rowan University
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)
S. Mandayam, ECE Dept., Rowan University
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
S. Mandayam, ECE Dept., Rowan University
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%
FRAP Chinese American
S. Mandayam, ECE Dept., Rowan University
Database IssuesDatabase Issues
• 93 images could not be analyzed.
S. Mandayam, ECE Dept., Rowan University
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.
S. Mandayam, ECE Dept., Rowan University
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%
S. Mandayam, ECE Dept., Rowan University
AcknowledgementsAcknowledgements
• “Dietary Patterns and Breast Density in Chinese-American Women,” American Cancer Society, Award Amount: $333,000; 2002– 2007.
• “Dietary Patterns and Breast Density,”American Institute of Cancer Research, Award Amount: $55,000; 2000 – 2002.
• “Digital Imaging Across the Curriculum,” National Science Foundation, Award Amount: $74,998, 2003-2005.
S. Mandayam, ECE Dept., Rowan University
DiscussionDiscussion