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

1

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

2

3

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

4

Present

5

Absent

6

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

Algorithm overview

8

Given a dermoscopy image

Original

Algorithm overview

9

Pre-processing. Using LoG sharp changes of colors are detected

Original Laplacian of Gaussian

Algorithm overview

10

Converting the result of the pre-processing to a graph.

Original Laplacian of Gaussian Image to Graph

Algorithm overview

11

Converting the result of the pre-processing to a graph.

Original Laplacian of Gaussian Image to Graph

Cyclic Subgraphs

Algorithm overview

12

Converting the result of the pre-processing to a graph.

Original Laplacian of Gaussian Image to Graph

Cyclic SubgraphsPigment Network

Algorithm overview

13

Converting the result of the pre-processing to a graph.

Original Laplacian of Gaussian Image to Graph

Cyclic SubgraphsPigment NetworkClassification

Present

Given Image

14

Filtered by Laplacian of Gaussian

15

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

3

8

4816

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

17

18

19

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

20

21

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

22

23

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%

24

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

Questions?

Thank you

26

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)

27

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

29

Original Laplacian of Gaussian Image to Graph

Cyclic SubgraphsPigment NetworkClassification

Present