Abstract
Skin conditions represent an enormous health care burden worldwide, and as datasets of skin images grow, there is continued interest in computerized approaches to analyze skin images. In order to explore and gain insights into datasets of skin images, we propose a graph based approach to visualize a progression of similar skin images between pairs of images. In our graph, a node represents both a clinical and dermoscopic image of the same lesion, and an edge between nodes captures the visual dissimilarity between lesions, where dissimilarity is computed by comparing the image responses of a pretrained convolutional neural network. We compute the geodesic/shortest path between nodes to determine a path of progressively visually similar skin lesions. To quantitatively evaluate the quality of the returned path, we propose metrics to measure the number of transitions with respect to the lesion diagnosis, and the progression with respect to the clinical 7-point checklist. Compared to baseline experiments, our approach shows improvements to the quality of the returned paths.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Argenziano, G., Fabbrocini, G., Carli, P., Vincenzo, D.G., Sammarco, E., Delfino, M.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134(12), 1563–1570 (1998)
Argenziano, G., Soyer, H.P., Giorgio, V.D., Piccolo, D., Carli, P., Delfino, M., Ferrari, A., Hofmann-Wellenhof, R., Massi, D., Mazzocchetti, G., Scal-venzi, M., Wolf, I.H.: Interactive atlas of dermoscopy: a tutorial (Book and CD-ROM) (2000)
Bunte, K., Biehl, M., Jonkman, M.F., Petkov, N.: Learning effective color features for content based image retrieval in dermatology. Pattern Recogn. 44(9), 1892–1902 (2011)
Duffy, K., Grossman, D.: The dysplastic nevus: from historical perspective to management in the modern era: Part I. Historical, histologic, and clinical aspects. J. Am. Acad. Dermatol. 67(1), 1–27 (2012)
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Hay, R.J., Johns, N.E., Williams, H.C., Bolliger, I.W., Dellavalle, R.P., Margolis, D.J., Marks, R., Naldi, L., Weinstock, M.A., Wulf, S.K., Michaud, C., Murray, J.L.C., Naghavi, M.: The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions. J. Invest. Dermatol. 134, 1527–1534 (2014)
Hegde, C., Sankaranarayanan, A.C., Baraniuk, R.G.: Learning manifolds in the wild. J. Mach. Learn. Res. 5037 (2012)
Jia, H., Wu, G., Wang, Q., Wang, Y., Kim, M., Shen, D.: Directed graph based image registration. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 175–183. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24319-6_22
Kawahara, J., BenTaieb, A., Hamarneh, G.: Deep features to classify skin lesions. In: IEEE ISBI, pp. 1397–1400 (2016)
Kawahara, J., Hamarneh, G.: Image content-based navigation of skin conditions. In: World Congress of Dermatology (2015)
Klingemann, M., Doury, S.: X Degrees of Separation (2016). https://artsexperiments.withgoogle.com/xdegrees/
Kogan, G.: Shortest path between images (2017). https://github.com/ml4a/ml4a-guides/blob/master/notebooks/image-path.ipynb
Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69–90 (2012)
Markovic, S., Erickson, L.A., Rao, R., Creagan, E.T., et al.: Malignant melanoma in the 21st century, Part 1: epidemiology, risk factors, screening, prevention, and diagnosis. Mayo Clin. Proc. 82(3), 364–380 (2007)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision (IJCV) 115(3), 211–252 (2015)
Schofield, J.K., Fleming, D., Grindlay, D., Williams, H.: Skin conditions are the commonest new reason people present to general practitioners in England and Wales. Br. J. Dermatol. 165, 1044–1050 (2011)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations Learning Representations (ICLR) (2015)
Acknowledgments
Thanks to the Natural Sciences and Engineering Research Council (NSERC) of Canada for funding and to the NVIDIA Corporation for the donation of a Titan X GPU used in this research. Thanks to Sara Daneshvar for preparing the data used in this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kawahara, J., Moriarty, K.P., Hamarneh, G. (2017). Graph Geodesics to Find Progressively Similar Skin Lesion Images. In: Cardoso, M., et al. Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics. GRAIL MICGen MFCA 2017 2017 2017. Lecture Notes in Computer Science(), vol 10551. Springer, Cham. https://doi.org/10.1007/978-3-319-67675-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-67675-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67674-6
Online ISBN: 978-3-319-67675-3
eBook Packages: Computer ScienceComputer Science (R0)