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Maryam Sadeghi1,3, Majid Razmara1, Martin Ester1,
Tim K. Lee1,2,3 and M. Stella Atkins1
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
SPIEMedical Imaging2010
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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 carcinoma Combined nevus Melanoma
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Dermoscopy
Pigment Network DetectionPresent (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
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Present
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Absent
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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
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Algorithm overview
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Given a dermoscopy image
Original
Algorithm overview
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Pre-processing. Using LoG sharp changes of colors are detected
Original Laplacian of Gaussian
Algorithm overview
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Converting the result of the pre-processing to a graph.
Original Laplacian of Gaussian Image to Graph
Algorithm overview
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Converting the result of the pre-processing to a graph.
Original Laplacian of Gaussian Image to Graph
Cyclic Subgraphs
Algorithm overview
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Converting the result of the pre-processing to a graph.
Original Laplacian of Gaussian Image to Graph
Cyclic SubgraphsPigment Network
Algorithm overview
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Converting the result of the pre-processing to a graph.
Original Laplacian of Gaussian Image to Graph
Cyclic SubgraphsPigment NetworkClassification
Present
Given Image
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Filtered by Laplacian of Gaussian
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A binary image has some connected componentsEach of them is converted to a graph (G)
Each pixel a node of G A unique label according to its coordinate
Graph Gi|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
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Graph GiConnected Component i
7x7
Removing Undesired Cycles
Labels of nodes coordinates in the imageMean intensity of meshes in the original imageGlobules and dots: Inside color darker than outside color
Inside Color Outside Color
Extended area by 2 pixelsTuning the Thresholds
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Pigment Network GraphA 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
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Image ClassificationDensity 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 => PresentDensity Ratio < Threshold => Absent
)log(* LesionSizeV
EDensity
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Experimental Results
Original Image LOG Edge Detector Cyclic Subgraphs Present
Original Image LOG Edge Detector Cyclic Subgraphs Absent
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%
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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
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Questions?
Thank you
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ConclusionA 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)
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Previous WorkComparing 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
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Original Laplacian of Gaussian Image to Graph
Cyclic SubgraphsPigment NetworkClassification
Present