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