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Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis

  • Sina KhakAbi
  • Tim K. Lee
  • M. Stella Atkins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

This paper presents a novel approach to analysis and classification of skin lesions based on their growth pattern. Our method constructs a tree structure for every lesion by repeatedly subdividing the image into sub-images using color based clustering. In this method, segmentation which is a challenging task is not required. The obtained multi-scale tree structure provides a framework that allows us to extract a variety of features, based on the appearance of the tree structure or sub-images corresponding to nodes of the tree. Preliminary features (the number of nodes, leaves, and depth of the tree, and 9 compactness indices of the dark spots represented by the sub-images associated with each node of the tree) are used to train a supervised learning algorithm. Results show the strength of the method in classifying lesions into malignant and benign classes. We achieved Precision of 0.855, Recall of 0.849, and F-measure of 0.834 using 3-layer perceptron and Precision of 0.829, Recall of 0.832, and F-measure of 0.817 using AdaBoost on a dataset containing 112 malignant and 298 benign lesion dermoscopic images.

Keywords

Tree Structure Dark Spot Dark Pixel Cluster Stage Preliminary Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sina KhakAbi
    • 1
    • 2
    • 3
  • Tim K. Lee
    • 1
    • 2
    • 3
  • M. Stella Atkins
    • 1
    • 2
  1. 1.School of Computing ScienceSimon Fraser UniversityCanada
  2. 2.Department of Dermatology and Skin ScienceUniversity of British Columbia and Vancouver Coastal Health Research InstituteCanada
  3. 3.Cancer Control Research ProgramBC Cancer Research CentreCanada

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