Melanoma detection on dermoscopic images using superpixels segmentation and shape-based features

In this work, we present a shape-based approach to automatic skin lesion segmentation and classification in dermoscopic images. We aim to differentiate three types of lesion 1) common nevi, 2) atypical nevi, and 3) melanomas by exploring the morphology features of segmented skin lesions. Our method is an attempt to design a computer-aided ABCDEs of melanoma, where the Asymmetry and Border components are estimated using morphological features. The lesions are first segmented using a super-pixel merging strategy with an RGB criterion. Later, the segmentation method was evaluated on the PH2 dataset, and compared with other state-of- the-art skin segmentation methods. The classification was also conducted on the PH2 dataset through a 10-fold cross-validation set-up with a training and testing set partition of 90% and 10% respectively. We employed logistic regression, SVM and a neural network as classification algorithms. The best performances was 86.5% on average with the neural network.

[1]  M. Faezipour,et al.  Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention , 2014, IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014.

[2]  N. Zhang,et al.  The role of the ugly duckling sign in patient education , 2017, Journal of the American Academy of Dermatology.

[3]  Huiyu Zhou,et al.  A State-of-the-Art Survey on Lesion Border Detection in Dermoscopy Images , 2015 .

[4]  John Willian Branch,et al.  Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging , 2018, MICCAI.

[5]  Jorge S. Marques,et al.  Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features , 2014, IEEE Systems Journal.

[6]  Gerald Schaefer,et al.  Lesion border detection in dermoscopy images , 2009, Comput. Medical Imaging Graph..

[7]  Jeremy S Bordeaux,et al.  Early detection of melanoma: reviewing the ABCDEs. , 2015, Journal of the American Academy of Dermatology.

[8]  Daniele Nardi,et al.  Skin lesion image segmentation using Delaunay Triangulation for melanoma detection , 2016, Comput. Medical Imaging Graph..

[9]  Jorge S. Marques,et al.  Melanoma detection algorithm based on feature fusion , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Silvia Biasotti,et al.  Mathematical Tools for Shape Analysis and Description , 2014, Synthesis Lectures on Computer Graphics and Animation.

[12]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  David Dagan Feng,et al.  Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[14]  Adel Al-Jumaily,et al.  The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[15]  Mohammad Aldeen,et al.  Automated colour identification in melanocytic lesions , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Punam K. Saha,et al.  A survey on skeletonization algorithms and their applications , 2016, Pattern Recognit. Lett..

[17]  Kpalma Kidiyo,et al.  A Survey of Shape Feature Extraction Techniques , 2008 .

[18]  Julie Ann A. Salido,et al.  Using Deep Learning for Melanoma Detection in Dermoscopy Images , 2018 .

[19]  D. Kendall.  SHAPE MANIFOLDS, PROCRUSTEAN METRICS, AND COMPLEX PROJECTIVE SPACES , 1984 .

[20]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[21]  Robert B. Fisher,et al.  A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions , 2013 .