1. Medizinische Informatik und Dokumentation 2004 Tissue Counter Analysis of histological images M. Wiltgen Institute of Medical Informatics, Statistics and Documentation MED UNI GRAZ

2. Medizinische Informatik und Dokumentation 2004 Steps in Image Analysis Preprocessing Segmentation Feature Extraction Classification Interpretation

3. Medizinische Informatik und Dokumentation 2004 Histological tissue: microscopic views In histological tissue the structures are arranged in a variety of patterns Segmentation of different structures is case dependent Segmentation cannot be done in a general approach

4. Medizinische Informatik und Dokumentation 2004 Study set: 80 cases from benign common nevi and malignant melanoma Malignant Melanoma Benign Common Nevi

5. Medizinische Informatik und Dokumentation 2004 Dissection into square elements The images are divided into square elements of equal size For each square element texture features are calculated

6. Medizinische Informatik und Dokumentation 2004 Material and Preparation Study set: Learning set (40 images) Test set (40 images) Preparation: Embedded in paraffin and cut at a section thickness of 4µm Stained with hematoxylin and eosin

7. Medizinische Informatik und Dokumentation 2004 T issue C ounter A nalysis : Overview Feature analysis and extraction Classification Relocation Feature analysis and extraction Steps

8. Medizinische Informatik und Dokumentation 2004 The features are based on: Histogram Co-occurrence matrix Variance Shape of the distribution Mean value Entropy of the grey level distribution Average values Moments Correlations Entropies

9. Medizinische Informatik und Dokumentation 2004 Histogram of grey levels Distribution of grey levels:

10. Medizinische Informatik und Dokumentation 2004 Features based on the histogram Mean value Standard deviation Skewness and Kurtosis Entropy of the grey level distribution

11. Medizinische Informatik und Dokumentation 2004 Co-occurrence matrix Interpixel relationships:

12. Medizinische Informatik und Dokumentation 2004 Features based on the co-occurrence matrix Angular Second Moment Summed Average Sum Variance Sum Entropy

13. Medizinische Informatik und Dokumentation 2004 Benign Common Nevi

14. Medizinische Informatik und Dokumentation 2004 Malignant Melanoma

15. Medizinische Informatik und Dokumentation 2004 Feature values for Benign Common Nevi and Malignant Melanoma FeaturesNzMn Mean value127.794.3 Standard deviation35.456.1 Skewness-0.750.59 Angular second moment 0.001540.00115 Sum average256.5188.7 Sum variance276.9260.3 Sum entropy6.727.77

16. Medizinische Informatik und Dokumentation 2004 Fourier Transform Decomposition into frequency components: Power spectrum:

17. Medizinische Informatik und Dokumentation 2004 Features based on Fourier Transform Mean of the power spectrum at equidistant radius:

18. Medizinische Informatik und Dokumentation 2004 Benign Common Nevi

19. Medizinische Informatik und Dokumentation 2004 Malignant Melanoma

20. Medizinische Informatik und Dokumentation 2004 Feature Extraction The images of the 80 cases are divided into square elements with 32x32 pixels. The data extracted from the single square elements consist of an n-dimensional vector of features:

21. Medizinische Informatik und Dokumentation 2004 T issue C ounter A nalysis : Steps Feature analysis and extraction Classification Relocation Classification

22. Medizinische Informatik und Dokumentation 2004 CART: Classification and Regression Trees In CART analysis the set of square elements is split into homogeneous terminal nodes. The method consists of the parts: selection of splits, decision if the node is a terminal node or a non- terminal node, the assignment of a terminal node to a class.

23. Medizinische Informatik und Dokumentation 2004 CART: Selection of Splits The set of data is recursively splitted by use of a threshold value into two sub sets ( ).

24. Medizinische Informatik und Dokumentation 2004 CART: Selection of Splits The splitting is chosen in that way that difference between the impurities of the parents and the children sets has maximum value.

25. Medizinische Informatik und Dokumentation 2004 CART: Stop Rule The decision if the node is a terminal node or a non- terminal node if given by the stop rule: The threshold is a measure of how homogenous the terminal nodes must be.

26. Medizinische Informatik und Dokumentation 2004 CART: Classification Tree

27. Medizinische Informatik und Dokumentation 2004 CART: Assignment to a Class All the subsets are collected, assigned to a class and the classes are given by class label attachment:

28. Medizinische Informatik und Dokumentation 2004 CART: Splitting Rules The rules, which split the set of squares into disjunctive nodes are called splitting rules. The splitting rules are guiding through the tree.

29. Medizinische Informatik und Dokumentation 2004 Classification Results: Square Elements ClassTotal number of elements Classifi cation Rate % Number of elements related to class 1 Number of elements related to class 2 1 benign common nevi 512092,7344748372 2 malignant melanoma 512092,090 4054715

30. Medizinische Informatik und Dokumentation 2004 Classification Results: Individual Cases Test set Learning set

31. Medizinische Informatik und Dokumentation 2004 Individual Cases of Malignant Melanoma in the Learning Set

32. Medizinische Informatik und Dokumentation 2004 Individual Cases of Benign Common Nevi in the Test Set

33. Medizinische Informatik und Dokumentation 2004 T issue C ounter A nalysis : Steps Feature analysis and extraction Classification Relocation

34. Medizinische Informatik und Dokumentation 2004 Relocation of the classification results

35. Medizinische Informatik und Dokumentation 2004 Erroneous Classification Results for Benign Common Nevi

36. Medizinische Informatik und Dokumentation 2004 Correct Classification results for malignant melanoma

37. Medizinische Informatik und Dokumentation 2004 Results and Discussion RESULTS: RESULTS: For the learning set and the test set there is a significant difference between benign nevi and malignant melanoma without overlap. Discriminant analysis based on the percentage of malignant elements facilitated a correct classification of all cases. DISCUSSION: DISCUSSION: Because no image segmentation was needed, problems related to this task were avoided. Though wrong classification of individual elements is unavoidable to some degree, tissue counter analysis shows a good discrimination between benign common nevi and malignant melanoma.

38. Medizinische Informatik und Dokumentation 2004 Conclusion Tissue counter analysis is a potential diagnostic tool in automatic or semi automatic discrimination of melanocytic skin tumors.