1. 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) Computer Science Final Year Project 2004

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

3. Introduction - 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 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

4. Step 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

5. 5.Statistic with multi-scale approach and co-occurrence matrix Step 1: Feature Extraction The six features: 1) The mean gray level - Inversely proportion to cirrhosis severity. - Affected by the area of normal tumor 2) The first percentile of the gray level distribution P First order statistic - Inversely proportion to cirrhosis severity. - Affected by the present of normal tumor Co-occurrence matrix statistic 3) Entropy: 4) Contrast: 5) Angular Second Moment: 6) Correlation FeaturesRelation of feature and cirrhosis status Physical meaning EntropyInversely proportion Randomness of intensity changes ContrastInversely proportion Edge detection ASMInversely proportion Homogeneity of image CorrelationProportion Similarity among pixel pairs 6) Morphological based method Segment out tumor structure from liver Count the number and circumference of tumor

6. 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: 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. The input features are normalized by standard score. Commonly used in image feature classification

7. Evaluation 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 SetClassifierAccuracy TypeSettingTypeParameters 2 Class Classification 3 Class Classification Plain wavelet coefficient 3 Level Haar KNNK=5 301/732 41.1202% 234/732 31.9672% Histogram of wavelet coefficient 2 Level Haar kNNKLD, k=12 548/772 75.9003% 431/772 59.6953% Statistic with “ Difference on Gaussians ” filter Filtering along X- direction kNNK=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 SetClassifierAccuracy TypeSettingTypeParameters 2 Class Classification 3 Class Classification multi-scale co- occurrence matrix 3 Resolution level kNNKLD, 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 2 Resolution Level kNNSSD, 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

8. Evaluation of algorithms The classifiers: Accuracy: >>> all of them have similar results. >>> Depends on features. Running time (including partition for 732 testing cases): Classifier2 classes3 classes kNN2s FFNN67s80s PNN7s Pros and Cons k-NN Fast Easy to implement Sensitive to class distribution of data set. Size of database is large and linearly increasing. FFNN Size 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

9. Conclusion 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 Extension 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