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Application of image processing Application of image processing techniques to tissue texture techniques to tissue texture
analysis and image compressionanalysis and image compressionAdvisor : Dr. Albert Chi-Shing CHUNG
Presented by Group ACH1Presented by Group ACH1(LAW Wai Kong and LAI Tsz Chung)(LAW Wai Kong and LAI Tsz Chung)
Computer Science Final Year Project 2004
OverviewOverview
• Introduction– Motivation– Objectives
• Results– Classification algorithms:
• Feature extraction & Classifier selection
– Software implementation:
• Conclusion • Future Extension• Question and Answer Session
IntroductionIntroduction - Motivation- Motivation
Diagnosis of cirrhosis:
1) Manual diagnosis of ultrasonic liver image
2) Histological analysis •Invasive
•Inaccurate •Results dependent on experience of sonographers
Both are time consuming
How about computer aided diagnosis system?
In what extent this system assist doctor?
- Objectives - Objectives 1. Designated user interface with support of ultrasonic image
compression•No pre-image processing is needed
•Reduce storage space
Facilitate the diagnosis process
2. Multi-severity level classification
•Cirrhosis treatment require severity information.
3. Machine independence
•Compatible with different ultrasound scanning machine
Challenge !! How to classify patients?
2 steps
Step 1: Feature ExtractionStep 1: Feature Extraction
Firstly, extract useful features from image.
We have examined several feature extraction approaches for performance comparison
The most accurate approach will be implemented in our system
1. Direct comparison of wavelet coefficient(Haar, Symlets, Daubechies)
2. Histogram of wavelet coefficient (Haar, Symlets, Daubechies)
3. Statistic with “Difference on Gaussians” filter
4. Direct comparison between multi-scale co-occurrence matrix
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Step 1: Feature ExtractionStep 1: Feature Extraction
The six features:
1) The mean gray level
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- Inversely proportion to cirrhosis severity. - Affected by the present of normal tumor
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Features Relation of feature and cirrhosis status
Physical meaning
Entropy Inversely proportion Randomness of intensity changes
Contrast Inversely proportion Edge detection
ASM Inversely proportion Homogeneity of image
Correlation Proportion Similarity among pixel pairs
6) Morphological based method
• Segment out tumor structure from liver• Count the number and circumference of tumor
• Input features: normalized to range between [0,1]• Category: normalized to range between [0,1]• Classification: by setting thresholds base on # category. • 1st layer: 5 hyperbolic tangent sigmoid transfer units• 2nd layer: 1 linear transfer unit• Train function: Levenberg-Marquardt back-propagation• Performance: MSE• Stopping threshold: 0.01• Maximum training cycle = 200
Step 2: ClassifierStep 2: Classifier
• Basic requirements: – Continuous learning
– Multi class classification (severity category)
– Robust
– Database can update per patient (one pattern). Secondly, classify patients based on extracted features
3 classifiers were examined
1) k-Nearest Neighbor Classifier
• Use the category of k-nearest neighbor in database to classify a new entry.
• The features are normalized by standard score.
• Distance-weighted.
• Choice of distance: SSD / KLD
• Physically, KLD measures relative entropy between PDF
2) Feed-forward Neural Network
• A direct continuation of the work on Bayes classifiers, which relies on Parzen windows classifiers.
Setting:
3) Probabilistic Neural Network
• It learns to approximate the PDF of the training examples.
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• Commonly used in image feature classification
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Evaluation of algorithmsEvaluation of algorithms
Method of evaluating hypothesis: 10-fold cross validation (in MatLab)
Problem: Images of the same patient have similar features!
Solution: Use patient ID to partition the data set.
Problem: uneven class distribution in folds!
Solution: Partition the patients based on their category, ensure class distribution is similar to original data set.
The features:
•Theoretically, morphology is a descriptive feature, but, practically, fine tuning of parameters is needed.•Segmentation parameter (sigma of Gaussian filter, initial marker intensity) too sensitive to suit all testing cases•Number of tumors was unreasonably fluctuated. (tumors count ranged from 15 to 90)
Comparison of best results among all features sets with different classifier:
Features Set Classifier Accuracy
Type Setting Type Parameters 2 Class Classification
3 Class Classification
Plain wavelet coefficient
3 Level
Haar
KNN K=5 301/732 41.1202%
234/732 31.9672%
Histogram of wavelet coefficient
2 Level
Haar
kNN KLD, k=12 548/772 75.9003%
431/772 59.6953%
Statistic with “Difference on Gaussians” filter
Filtering along X-direction
kNN K=19 531/772 (72.541%)
434/772 (59.2896%)
PNN 447/772 (72.541%)
396/772 (54.0984%)
FFNN 497/772 (67.8962%)
442/772 (60.3825%)
Features Set Classifier Accuracy
Type Setting Type Parameters 2 Class Classification
3 Class Classification
multi-scale co-occurrence matrix
3 Resolution level
kNN KLD, k=3 312/515 60.5825%
219/515 45.5242%
SSD, k=2 284/515 55.1456%
211/515 40.9709%
Statistic of multi-resolution and co-occurrence matrix
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Resolution Level
kNN SSD, k=19 614/732 83.8798%
511/732 69.8087%
PNN 607/732 82.9235%
497/732 67.8962%
FFNN 619/732 84.5628%
508/732 69.3989%
The data set is captured by Dr. Simon Yu, consultant and adjunct associate professor from Department of Diagnostic Radiology and Organ Imaging, Prince of Wales Hospital
Evaluation of algorithmsEvaluation of algorithmsThe classifiers:
Accuracy:>>> all of them have similar results. >>> Depends on features.
Running time (including partition for 732 testing cases):
Classifier 2 classes 3 classes
kNN 2s 2s
FFNN 67s 80s
PNN 7s 7s
Pros and Cons
k-NN
FastEasy to implement
Sensitive to class distribution of data set.Size of database is large and linearly increasing.
FFNNSize of database is a small constant.Robust
Training is slow. (> 40 times of k-NN) Should update per epoch to prevent noise.
PNN
Fast
Highly sensitive to class distribution of data set. Size of database increases linearly.
k-NN
ConclusionConclusion
• Developed a designated classification system that can contribute to medical aspect
• Examined different machine independent classification algorithms for multi-severity classification
• Proposed utilization of multi-resolution statistic with co-occurrence matrix for cirrhosis detection
• Realized machine learning and image processing techniques in a real life situation
• Explored the knowledge about cirrhosis and liver
Future ExtensionFuture Extension
• Clustering of features
• Fine tuning the parameters of morphological approach
• Histological findings of cases will be able to improve our system