Transcript Binning Strategies for Tissue Texture Extraction in DICOM Images

Binning Strategies for Tissue Texture
Extraction in DICOM Images
CTI Students: Bikash Bhattacharyya, Kriti Jauhar
Advisors: Dr. Daniela Raicu, Dr. Jacob Furst
Submitted To: RSNA Conference ‘05, Chicago, IL
Why Binning ?
 Binning Definition:
Putting gray-levels into bins for image compression
e.g. 1,2,3,4 gray levels in Bin 1
5,6,7,8 gray levels in Bin 2
DICOM Images – 12 Bit - 4096 intensities
Texture Feature Calculation – All intensities - SLOW
Binning allows for additional flexibility to trade off large
intensity ranges against computational speed
COMPUTATION PERFORMANCE
BACK BONE
HEART
2
8
6
CPU
ELASPED
4
SECONDS
SECONDS
10
1.5
2
0.5
0
0
32
64
128
256
BIN
512
1024
CPU
1
ELAPSED
32
64
128
256
BIN
512
1024
Linear Binning
 Linear Binning - Bins of equal size
256 bins for DICOM images produces bin
ranges [0..15] , [16..31] ,…[4081..4096]
 Quick and Efficient approach
 Pre -processing step for Harlick texture feature
calculation
 Promising results for classification of tissues
based on Haralick texture features
Disadvantages of Linear Binning
 Soft tissues with similar intensities may end up in
the same bin with linear binning
 Soft tissues misclassification
 Accuracy of Liver and Spleen not very high
 Computed Tomography images contain low
number of pixel in the range [1500 – 4096]
Non-Linear Binning – Is it possible to improve
accuracy of soft tissues?
Analysis of Linear Binning (contd.)
EXAMPLES
Liver
Spleen
PROCESS SUMMARY
IMAGE DB
(1374 Images from
2 Patients)
LINEAR BINNING
BINS= 256
BINNING
NON LINEAR
BINNING
CLIPPED
BINNING
BINS= 258
CO-OCCURRENCE
MATRICES & TEXTURE
DESCRIPTORS
CLASSIFICATION
MODEL
(DECISION TREES)
K-MEANS CLUSTERING
K=256
EUCLIDEAN DISTANCE
EVALUATE
RESULTS
Two Approaches of Non-Linear Binning
Clipped Binning based on visual inspection of gray levels
Range [0, 856] mapped to Bin 1
Range [1368 , 4096] mapped to Bin 258
Range [856, 1368] mapped to 256 linear bins
e.g. 856 to 858 gray levels in Bin 123
Non Linear Binning based on K-Means Clustering
256 Clusters – Compare results of 256 linear-bins
Distance Measure – Euclidean
Clusters of Gray Level Ranges
Gray Level ranges form Non-linear Bins
Non-linear Binning using K-Means
Process Flow
PATIENT IMAGES
141 FILES
COMPUTE FREQUENCY OF
GRAY LEVELS FOR EACH
IMAGE
DISTANCE MEASURE
NO OF CLUSTERS
BIN SELECTED IMAGE
NO
4096 BY 141 VECTORS
REPRESENTING ALL IMAGES
K-MEANS CLUSTERS
IDENTIFICATION
IS ANY GRAY LEVEL
CLUSTER LESS THAN MIN
BIN SIZE?
COMBINE WITH NEXT
GRAY LEVEL RANGE
K-Means





K =256
141 Dimensions/Images
4096 points/Gray Levels
Initial Points /Random Centroids
Similarity Metric = Euclidean Distance
CLUSTER
Start GL
End
14
462
884
14
1363
1365
14
1367
1537
14
1539
1539
14
1543
1543
139
1538
1538
139
1540
1542
139
1544
4095
Issues
 263 Unique Gray Levels Identified
 Multiple Gray Levels – Identified in one Cluster
e.g. Cluster 14 has gray levels from 462 to 884
Cluster 14 also has gray levels from 1540 to 1542
GL
Classification Results
TRAINING SET
TRAINING-K-Mean s Spleen
Sensitivity
Specificity
Precision
Accuracy
92.23%
100.00%
91.35%
98.12%
TRAINING-Clipped Spleen
Sensitivity
Specificity
Precision
Accuracy
88.05%
99.07%
95.24%
97.14%
Backbone Kidney Heart
Liver
Total
98.10%
92.36%
96.38%
94.08%
94.63%
96.46%
99.48%
99.09%
99.87%
98.98%
95.01%
97.08%
95.00%
99.31%
95.55%
97.13%
98.01%
98.68%
98.90%
98.17%
Backbone Kidney
Heart
Liver
Total
99.19%
92.59%
97.89%
89.42%
93.43%
99.81%
98.19%
99.35% 100.00%
99.28%
99.73%
89.93%
96.53%
83.04%
92.89%
99.56%
98.02%
99.12%
96.70%
98.11%
TRAINING Linear Binning Spleen Backbone Kidney Heart
Liver
Total
Sensitivity
79.50%
99.70% 92.70% 84.60% 80.00% 87.30%
Specificity
99.50%
99.50% 97.90% 98.50% 96.90% 98.46%
Precision
73.60%
99.20% 89.70% 90.60% 83.80% 87.38%
Accuracy
94.10%
99.60% 97.10% 96.50% 94.10% 96.28%
Classification Results
TESTING SET
TESTING-KMEANS Liver
Sensitivity
Specificity
Precision
Accuracy
91.03%
96.99%
86.59%
95.94%
TESTING-Clipped Liver
Sensitivity
Specificity
Precision
Accuracy
52.17%
100.00%
15.58%
83.91%
Backbone Kidney
Heart
Spleen Total
93.97%
88.00%
84.15%
74.58%
86.35%
96.72%
97.61%
98.06% 100.00%
97.88%
95.90%
68.75%
90.79%
75.86%
83.58%
95.49%
97.52%
95.49%
93.45%
95.58%
Backbone Kidney Heart
Spleen Total
98.84% 76.81% 85.25% 31.21% 68.85%
92.52% 95.47% 94.81% 97.85% 96.17%
88.54% 73.61% 70.27% 84.62% 66.52%
94.85% 93.13% 93.56% 77.68% 88.63%
TESTING- Linear Binning Liver
Backbone Kidney Heart
Spleen Total
Sensitivity
73.80% 100.00% 86.20% 73.60% 70.50% 80.82%
Specificity
95.90%
97.60% 97.80% 97.20% 95.10% 96.72%
Precision
76.20%
96.80% 87.50% 84.10% 62.00% 81.32%
Accuracy
92.50%
98.60% 96.00% 93.20% 92.50% 94.56%
Graphical User Interface
Conclusion
Non-Linear Binning with K-Means gave
us the best overall results ( 86.35%)
Results for Liver and Spleen improved from
73.80% to 91.03% for liver and 70.50% to 74.58%
for spleen
Clipped Binning performed poorly on testing set
with overall sensitivity of only 68.85%
Results with K-Means improved over Linear
Binning
Future Work
Experimenting with bins other than 256 such as:
64, 128 etc.
Exploring other similarity measures such as:
Jeffrey Divergence, Mahalanobis Distance etc.
Testing other classification algorithms besides
decision trees, such as: Neural Networks,
Bayesian Networks, Logistic Regression etc.
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