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23 February 2012 Multi-level feature extraction for skin lesion segmentation in dermoscopic images
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Abstract
This paper presents a novel approach in computer aided skin lesion segmentation of dermoscopic images. We apply spatial and color features in order to model the lesion growth pattern. The decomposition is done by repeatedly clustering pixels into dark and light sub-clusters. A novel tree structure based representation of the lesion growth pattern is constructed by matching every pixel sub-cluster with a node in the tree structure. This model provides a powerful framework to extract features and to train models for lesion segmentation. The model employed allows features to be extracted at multiple layers of the tree structure, enabling a more descriptive feature set. Additionally, there is no need for preprocessing such as color calibration or artifact disocclusion. Preliminary features (mean over RGB color channels) are extracted for every pixel over four layers of the growth pattern model and are used in association with radial distance as a spatial feature to segment the lesion. The resulting per pixel feature vectors of length 13 are used in a supervised learning model for estimating parameters and segmenting the lesion. A dataset containing 116 challenging images from dermoscopic atlases is used to validate the method via a 10-fold cross validation procedure. Results of segmentation are compared with six other skin lesion segmentation methods. Our method outperforms ve other methods and performs competitively with another method. We achieve a per-pixel sensitivity/specicity of 0.890 and 0.901 respectively.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sina Khakabi, Paul Wighton, Tim K. Lee, and M. Stella Atkins "Multi-level feature extraction for skin lesion segmentation in dermoscopic images", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150E (23 February 2012); https://doi.org/10.1117/12.911664
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