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IFIP International Conference on Artificial Intelligence Applications and Innovations

AIAI 2015: Artificial Intelligence Applications and Innovations pp 45–60Cite as

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Automated Detection of Streaks in Dermoscopy Images

Automated Detection of Streaks in Dermoscopy Images

  • K. Delibasis19,
  • K. Kottari19 &
  • I. Maglogiannis20 
  • Conference paper
  • First Online: 15 November 2015
  • 1291 Accesses

  • 5 Citations

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 458)

Abstract

In this paper we present a novel algorithm for the detection of dark linear structures, which appear in digital dermoscopy images of skin lesions and they are called as streaks in relevant literature. The proposed algorithm is capable of detecting such linear structures using local image curvature information obtained by the Hessian matrix. A linear structure is characterized as streak, based on its geometric characteristics. The streak detection algorithm is applied to a number of dermoscopy images containing malignant and non-malignant skin lesions. In this work we propose also a new class of streak based image features and we investigate their value in image classification according to malignancy.

Keywords

  • Computer vision
  • Skin lesions
  • Image Segmentation
  • Streaks detection
  • Dermoscopy images
  • Melanoma classification

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References

  1. Reed, K.B., Brewer, J.D., Lohse, C.M., Bringe, K.E., Pruit, C.N., Gibson, L.E.: Increasing Incidence of Melanoma Among Young Adults: An Epidemiological Study in Olmsted County, Minnesota. Mayo Clinic Proceedings 87(4), 328–334 (2012)

    CrossRef  Google Scholar 

  2. Stern, R.S.: Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol. 146(3), 279–282 (2010)

    CrossRef  Google Scholar 

  3. Rogers, H.W., Weinstock, M.A., Harris, A.R., et al.: Incidence estimate of nonmelanoma skin cancer in the United States, 2006. Arch Dermatol. 146(3), 283–287 (2010)

    CrossRef  Google Scholar 

  4. American Cancer Society. Cancer Facts & Figures (2015). http://www.cancer.org/research/cancerfactsstatistics/cancerfactsfigures2015/ (accessed May 12, 2015)

  5. Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 13(5), 721–733 (2009)

    CrossRef  Google Scholar 

  6. Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: A review. Artificial Intelligence in Medicine 56(2), 69–90 (2012)

    CrossRef  Google Scholar 

  7. Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.: Oriented pattern analysis for streak detection in dermoscopy images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 298–306. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  8. Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.: Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions. IEEE T. on Medical Imaging 32(5), 849–861 (2013)

    CrossRef  Google Scholar 

  9. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A Comparison of Affine Region Detectors. International Journal of Computer Vision 65(1-2), 43–72 (2005)

    CrossRef  Google Scholar 

  10. Tuytelaars, T., Mikolajczy, K.: Local Invariant Feature Detectors: A Survey. Computer Graphics and Vision 3(3), 177–280 (2007)

    CrossRef  Google Scholar 

  11. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)

    CrossRef  Google Scholar 

  12. Aylward, S., Bullitt, E.: Initialization, noise, singularities, and scale in height-ridge traversal for tubular object centerline extraction. IEEE Transactions on Medical Imaging 21, 61–75 (2002)

    CrossRef  Google Scholar 

  13. Martinez-Perez, E., Hughes, A., Thom, S., Bharath, A., Parker, K.: Segmentation of blood vessels from red-free and fluorescein retinal images. Medical Image Analysis 11, 47–61 (2007)

    CrossRef  Google Scholar 

  14. Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 30(2), 117–156 (1996)

    CrossRef  Google Scholar 

  15. Maglogiannis, I., Delibasis, K.: Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy. Computer Methods and Programs in Biomedicine 118(2), 124–133 (2015)

    CrossRef  Google Scholar 

  16. Steger, C.: Extracting lines using differential geometry and Gaussian smoothing. International Archives of Photogrammetry and Remote Sensing 16(B3), 821–826 (1996)

    Google Scholar 

  17. Kovesi, P.: MATLAB and Octave functions for computer vision and image processing. Centre for Exploration Targeting, The University of Western Australia. http://www.csse.uwa.edu.au/~pk/research/matlabfns/

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Biomedical Informatics, Univ. of Thessaly, Thessaly, Greece

    K. Delibasis & K. Kottari

  2. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    I. Maglogiannis

Authors
  1. K. Delibasis
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  2. K. Kottari
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  3. I. Maglogiannis
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Corresponding author

Correspondence to K. Delibasis .

Editor information

Editors and Affiliations

  1. Univ. de Pau et des Pays de l'Adour (UPPA), Anglet, France

    Richard Chbeir

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

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© 2015 IFIP International Federation for Information Processing

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Cite this paper

Delibasis, K., Kottari, K., Maglogiannis, I. (2015). Automated Detection of Streaks in Dermoscopy Images. In: Chbeir, R., Manolopoulos, Y., Maglogiannis, I., Alhajj, R. (eds) Artificial Intelligence Applications and Innovations. AIAI 2015. IFIP Advances in Information and Communication Technology, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-319-23868-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-23868-5_4

  • Published: 15 November 2015

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23867-8

  • Online ISBN: 978-3-319-23868-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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