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Skin Cancer Surface Shape Based Classification Steven McDonagh March 10, 2008 Steven McDonagh Skin Cancer Surface Shape Based Classification Motivation A non-trivial classification problem Clinicians time is valuable Good track


  1. Skin Cancer Surface Shape Based Classification Steven McDonagh March 10, 2008 Steven McDonagh Skin Cancer Surface Shape Based Classification

  2. Motivation ◮ A non-trivial classification problem ◮ Clinician’s time is valuable ◮ Good track record if caught early enough Steven McDonagh Skin Cancer Surface Shape Based Classification

  3. Hypothesis A classification system using a combination of standard and depth based image features is more successful at the task of classifying skin lesion images than a system which uses standard image features alone. Steven McDonagh Skin Cancer Surface Shape Based Classification

  4. The system Steven McDonagh Skin Cancer Surface Shape Based Classification

  5. The system ◮ Data capture and pre-processing ◮ Feature calculation and selection ◮ Training and classification Steven McDonagh Skin Cancer Surface Shape Based Classification

  6. Data capture Figure: Stereo-scopic geometry Figure: Stereo camera rig Steven McDonagh Skin Cancer Surface Shape Based Classification

  7. Data capture Figure: Lesion colour data Figure: Lesion reconstruction from range data Steven McDonagh Skin Cancer Surface Shape Based Classification

  8. Pre-processing Figure: Sample 2 Figure: Sample 1 Steven McDonagh Skin Cancer Surface Shape Based Classification

  9. Pre-processing Figure: Sample 2 Figure: Sample 1 Steven McDonagh Skin Cancer Surface Shape Based Classification

  10. Pre-processing ◮ Image Segmentation: Separate lesion from surrounding skin ◮ Automated thresholding techniques ◮ Do it manually! Figure: Hand segmented image Steven McDonagh Skin Cancer Surface Shape Based Classification

  11. The system ◮ Data capture and pre-processing ◮ Feature calculation and selection ◮ Training and classification Steven McDonagh Skin Cancer Surface Shape Based Classification

  12. Feature calculation ◮ What is a feature? ◮ Standard 2D image based features ◮ A symmetry ◮ B order irregularity ◮ C olour variegation ◮ D iameter Figure: Sample features feature vector X = [ − 2 . 42 , 412 , 0 . 63 , 50 . 75] Steven McDonagh Skin Cancer Surface Shape Based Classification

  13. 3D features ◮ ∆ spotheight (avg spot z depth − avg skin z depth) Figure: ∆ spotheight σ [ z ] ◮ R z = spot local texture σ [ z ] skin roughness ratio ◮ Peak and pit density # peaks + # pits spot area Steven McDonagh Skin Cancer Surface Shape Based Classification

  14. Investigating a feature ∆ spotheight Figure: 1D Scatter plot for i1 feature Steven McDonagh Skin Cancer Surface Shape Based Classification

  15. Investigating a feature ∆ spotheight Figure: Scatter plot for i1 feature Steven McDonagh Skin Cancer Surface Shape Based Classification

  16. Feature selection ◮ n features? ⇒ 2 n subset combinations! ◮ How do we select the best feature subset? ◮ Feature subset selection ◮ Sieve out the irrelevant / redundant features ◮ Goal : Small subset of features that give high predictive accuracy ◮ For any given image we can now compute a feature vector X = [ f 1 , f 2 , ..., f m ] m ≤ n Steven McDonagh Skin Cancer Surface Shape Based Classification

  17. Figure: Evolution of subset classification accuracy Steven McDonagh Skin Cancer Surface Shape Based Classification

  18. The system ◮ Data capture and pre-processing ◮ Feature calculation and selection ◮ Training and classification Steven McDonagh Skin Cancer Surface Shape Based Classification

  19. Training and classification ◮ Goal : Given a previously unseen lesion image X ⇒ compute the most likely class k that it belongs to ◮ k ∈ { Basel cell carcinoma, Squamous cell carcinoma, Seborrheic keratosis, Melanocytic naevus, Actinic keratosis } ◮ We need to find P ( class = k | X ) for each class k ◮ Classify new image data X as: 1. Class k which provides highest conditional probability 2. Assign sample X to the class j which minimises the quantity Σ k L kj P ( class = k | X ) Steven McDonagh Skin Cancer Surface Shape Based Classification

  20. Experimental results Diagnosis AK BCC ML SCC SK AK 11 0 0 0 0 100% BCC 0 57 1 4 3 87.6% True ML 0 4 48 2 7 78.6% SCC 0 4 1 19 1 76% SK 0 4 8 5 55 76.3% Overall accuracy 83.7% Misclassification cost 196 Table: Best feature set found by accuracy based criterion Steven McDonagh Skin Cancer Surface Shape Based Classification

  21. Experimental results Diagnosis AK BCC ML SCC SK AK 11 0 0 0 0 100% BCC 0 47 3 14 1 72.3% True ML 0 4 48 4 5 78.6% SCC 0 0 1 23 1 92% SK 0 6 15 5 46 63.8% Overall accuracy 81.3% Misclassification cost 183 Table: Best feature set found by cost based criterion Steven McDonagh Skin Cancer Surface Shape Based Classification

  22. Experimental results Diagnosis AK BCC ML SCC SK AK 11 0 0 0 0 100% BCC 0 55 3 2 5 84.6% True ML 0 7 49 0 5 80.3% SCC 0 9 2 11 3 44% SK 0 7 8 1 56 77.7% Overall accuracy 77.3% Misclassification cost 306 Table: Best feature set found when constrained to colour feature pool Steven McDonagh Skin Cancer Surface Shape Based Classification

  23. Discussion ◮ Novel data capture techniques may provide us with useful information ◮ Feature calculation and selection methods are just as important as data quality ◮ Success measure is vital! Steven McDonagh Skin Cancer Surface Shape Based Classification

  24. Thanks for listening! ◮ Questions? Steven McDonagh Skin Cancer Surface Shape Based Classification

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