1. Maryam Sadeghi 1,3, Majid Razmara 1, Martin Ester 1, Tim K. Lee 1,2,3 and M. Stella Atkins 1 1: School of Computing Science, Simon Fraser University 2: Department of Dermatology and Skin Science, University of British Columbia 3:Cancer Control Research, BC Cancer Agency Graph-based Pigment Network Detection in Skin Images SPIE Medical Imaging 2010 1

2. Skin cancer : most common of all cancers Melanoma : leading cause of mortality Early detection: significantly reduces mortality Skin cancer and melanoma [ Images courtesy of “Dermoscopy of pigmented skin lesions” ] Basal cell carcinomaCombined nevusMelanoma 2

3. 3 Dermoscopy

4. Pigment Network Detection Present (Typical or Atypical Pigment Network ) Typical : “light to dark-brown network with small, uniformly spaced network holes and thin network lines distributed more or less regularly throughout the lesion and usually thinning out at the periphery” Atypical: “black, brown or gray network with irregular holes and thick lines“ Absent: There is no typical or atypical pigment network 4

5. Present 5

6. Absent 6

7. Problem Statement and Motivation Problem: Pigment network detection in dermoscopy images Motivation: Skin texture analysis for computer-aided diagnosis Pigment Network Visualisation for Training purposes 7

8. Algorithm overview 8 Given a dermoscopy image Original

9. Algorithm overview 9 Pre-processing. Using LoG sharp changes of colors are detected OriginalLaplacian of Gaussian

10. Algorithm overview 10 Converting the result of the pre-processing to a graph. OriginalLaplacian of GaussianImage to Graph

11. Algorithm overview 11 Converting the result of the pre-processing to a graph. OriginalLaplacian of GaussianImage to Graph Cyclic Subgraphs

12. Algorithm overview 12 Converting the result of the pre-processing to a graph. OriginalLaplacian of GaussianImage to Graph Cyclic SubgraphsPigment Network

13. Algorithm overview 13 Converting the result of the pre-processing to a graph. OriginalLaplacian of GaussianImage to Graph Cyclic SubgraphsPigment NetworkClassification Present

14. Given Image 14

15. Filtered by Laplacian of Gaussian 15

16. A binary image has some connected components Each of them is converted to a graph (G) Each pixel a node of G A unique label according to its coordinate Graph G i | V|= size of the connected component i in pixels |E|=Number of edges connecting the white pixels |V|=17 |E|=17 Iterative Loop Counting Algorithm. Binary Image to Graph Conversion 3 8 48 16 Graph G i Connected Component i 7x7

17. Removing Undesired Cycles Labels of nodes coordinates in the image Mean intensity of meshes in the original image Globules and dots: Inside color darker than outside color Inside Color Outside Color Extended area by 2 pixels Tuning the Thresholds 17

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20. Pigment Network Graph A new graph representing the Pigment Network Centers of the detected cycles ( green holes in the image) are determined as nodes For each center the distance from all nodes is computed According to the size of the lesion and the average size of the net holes, Maximum Distance Threshold (MDT) is set Two nodes are connected together if they are closer than MDT 20

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22. Image Classification Density Ratio of the detected pigment network Lesion Size: Size of the area of the image that is inspected for finding the pigment network Density Threshold Density Ratio ≥ Threshold => Present Density Ratio Absent 22

23. 23 Experimental Results Original Image LOG Edge Detector Cyclic Subgraphs Present Original Image LOG Edge Detector Cyclic Subgraphs Absent

24. Evaluation Data Set and Result: A set of 100 dermoscopic images used for tuning the parameters and thresholds of the method 500 images of size 768x512 are used to test the performance of the method Taken from Argenziano et al.’s Interactive Atlas of Dermoscopy Each image is labeled as ‘Absent’ or ‘Present {typical, atypical} Accuracy: 92.6% 24

25. Future Work Features of pigment networks Color, regularity, thickness, spatial arrangement Extending the classification to 3 classes of Absent, Typical, and Atypical Color of the of surrounding network in blue channel Thickness and irregularity of the network Modifying the method to find other dermoscopic structures and patterns 25

26. Questions? Thank you 26

27. Conclusion A novel graph-based method for classifying and visualizing pigment networks. Evaluating its ability to classify and visualize real dermoscopic images The accuracy of the method is 92.6% (classifying images to Absent and Present) 27

28. Previous Work Comparing our results to previous methods: Anantha et al. “Detection of pigment network in dermatoscopy images using texture analysis”, 2004, Accuracy: 80% Betta et al. “Dermoscopic image-analysis system: estimation of atypical pigment network and atypical vascular pattern”, 2006, Recall:50%, Precision: 100%, F-measure: 66.66% Our method: Accuracy: 92.6% 28

29. 29 OriginalLaplacian of GaussianImage to Graph Cyclic SubgraphsPigment NetworkClassification Present