Application of image processing techniques to tissue texture analysis and image compression

1. Computer Science Final Year Project 2004 Application of image processing techniques to tissue texture analysis and image compression Advisor : Dr. Albert Chi-Shing CHUNG Presented by Group ACH1 (LAW Wai Kong and LAI Tsz Chung)

2. Overview • Introduction • Motivation • Objectives • Results • Classification algorithms: • Feature extraction & Classifier selection • Software implementation: • Conclusion • Future Extension • Question and Answer Session

3. Introduction - Objectives - Motivation • Designated user interface with support of ultrasonic image compression Diagnosis of cirrhosis: • No pre-image processing is needed Challenge !! How to classify patients? How about computer aided diagnosis system? • Reduce storage space 1) Manual diagnosis of ultrasonic liver image Facilitate the diagnosis process • Inaccurate • Results dependent on experience of sonographers • Multi-severity level classification Both are time consuming In what extent this system assist doctor? 2 steps • Cirrhosis treatment require severity information. 2) Histological analysis • Invasive • Machine independence • Compatible with different ultrasound scanning machine

4. Step 1: Feature Extraction • Direct comparison of wavelet coefficient(Haar, Symlets, Daubechies) • Direct comparison between multi-scale co-occurrence matrix We have examined several feature extraction approaches for performance comparison • Histogram of wavelet coefficient (Haar, Symlets, Daubechies) Firstly, extract useful features from image. The most accurate approach will be implemented in our system • Statistic with “Difference on Gaussians” filter

5. Step 1: Feature Extraction • Statistic with multi-scale approach and co-occurrence matrix 6) Morphological based method • Segment out tumor structure from liver • Count the number and circumference of tumor The six features: First order statistic Co-occurrence matrix statistic 1) The mean gray level 3) Entropy: 2) The first percentile of the gray level distribution P 4) Contrast: 5) Angular Second Moment: - Inversely proportion to cirrhosis severity. - Affected by the area of normal tumor - Inversely proportion to cirrhosis severity. - Affected by the present of normal tumor 6) Correlation

6. Step 2: Classifier • Basic requirements: • Continuous learning • Multi class classification (severity category) • Robust • Database can update per patient (one pattern). 3) Probabilistic Neural Network 1) k-Nearest Neighbor Classifier 2) Feed-forward Neural Network • Commonly used in image feature classification • A direct continuation of the work on Bayes classifiers, which relies on Parzen windows classifiers. • Use the category of k-nearest neighbor in database to classify a new entry. Setting: • It learns to approximate the PDF of the training examples. • The features are normalized by standard score. • 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 Secondly, classify patients based on extracted features • Distance-weighted. • Choice of distance: SSD / KLD • The input features are normalized by standard score. 3 classifiers were examined • Physically, KLD measures relative entropy between PDF

7. Evaluation of algorithms The features: Method of evaluating hypothesis: 10-fold cross validation (in MatLab) Comparison of best results among all features sets with different classifier: 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 • 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 Problem: Images of the same patient have similar features! Solution: Use patient ID to partition the data set. • Number of tumors was unreasonably fluctuated. (tumors count ranged from 15 to 90) Problem: uneven class distribution in folds! Solution: Partition the patients based on their category, ensure class distribution is similar to original data set.

8. Evaluation of algorithms The classifiers: Pros and Cons Accuracy: FFNN k-NN PNN • k-NN >>> all of them have similar results. >>> Depends on features. • Size of database is a small constant. • Robust • Fast • Fast • Easy to implement Running time (including partition for 732 testing cases): • Highly sensitive to class distribution of data set. • Size of database increases linearly. • Training is slow. (> 40 times of k-NN) • Should update per epoch to prevent noise. • Sensitive to class distribution of data set. • Size of database is large and linearly increasing.

9. Conclusion Future Extension • 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 • Clustering of features • Fine tuning the parameters of morphological approach • Histological findings of cases will be able to improve our system

10. Question and Answer Session