Border Detection of Skin Lesions on a Single System on Chip

  • Peyman Sabouri
  • Hamid GholamHosseini
  • John Collins
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)


High speed image processing is becoming increasingly important in medical imaging. Using the state-of-the-art ZYNQ-7000 system on chip (SoC) has made it possible to design powerful vision systems running software on an ARM processor and accelerating it from hardware resources on a single chip. In this paper, we take the advantage of accelerating an embedded system design on a single SoC, which offers the required features for real-time processing of skin cancer images. Different edge detection approaches such as Sobel, Kirsch, Canny and LoG have been implemented on ZYNQ-7000 for border detection of skin lesions, which can be used in early diagnosis of melanoma. The results show that the extended 5 × 5 canny edge detection implemented on the proposed embedded platform has better performance in compare with other reported methods. The performance evaluation of this approach has shown good processing time of 60 fps for real time applications.


Border detection Edge detection ZYNQ-7000 Medical imaging 


  1. 1.
    Diepgen T, Mahler V (2002) The epidemiology of skin cancer. Br J Dermatol 146:1–6CrossRefGoogle Scholar
  2. 2.
    Kopf AW, Salopek TG, Slade J, Marghoob AA, Bart RS (1995) Techniques of cutaneous examination for the detection of skin cancer. Cancer 75:684–690CrossRefGoogle Scholar
  3. 3.
    Goldsmith LA, Askin FB, Chang AE, Cohen C, Dutcher JP, Gilgor RS, Green S, Harris EL, Havas S, Robinson JK (1992) Diagnosis and treatment of early melanoma. JAMA: J Am Med Assoc 268:1314–1319CrossRefGoogle Scholar
  4. 4.
    Argenziano G, Soyer HP, Chimenti S, Talamini R, Corona R, Sera F, Binder M, Cerroni L, De Rosa G, Ferrara G (2003) Dermoscopy of pigmented skin lesions: results of a consensus meeting via the internet. J Am Acad Dermatol 48:679CrossRefGoogle Scholar
  5. 5.
    Xu L, Jackowski M, Goshtasby A, Roseman D, Bines S, Yu C, Dhawan A, Huntley A (1999) Segmentation of skin cancer images. Image Vis Comput 17:65–74CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Chen J, Cong J, Yan M, Zou Y (2011) FPGA-accelerated 3D reconstruction using compressive sensing. In: Proceedings of the ACM/SIGDA international symposium on field programmable gate arrays, pp 163–166Google Scholar
  8. 8.
    Noguera J, Neuendorffer S, Van Haastregt S, Barba J, Vissers K, Dick C (2011) Implementation of sphere decoder for MIMO-OFDM on FPGAs using high-level synthesis tools. Analog Integr Circ Sig Process 69:119–129CrossRefGoogle Scholar
  9. 9.
    Cong J, Zhang P, Zou Y (2011) Combined loop transformation and hierarchy allocation for data reuse optimization. In: IEEE/ACM international conference on computer-aided design (ICCAD), pp 185–192Google Scholar
  10. 10.
    Setayesh M, Mengjie Z, Johnston M (2012) Effects of static and dynamic topologies in particle swarm optimisation for edge detection in noisy images. In: IEEE congress on evolutionary computation (CEC), pp 1–8Google Scholar
  11. 11.
    Kekre DHB, Gharge MSM (2010) Image segmentation using extended edge operator for mammographic images. Int J Comput Sci Eng 2:1086–1091Google Scholar
  12. 12.
    Dhawan AP (2011) Medical image analysis. Wiley-IEEE PressGoogle Scholar
  13. 13.
    Canny J (1986) A computational approach to edge detection. IEEE transactions on PAMI-8 pattern analysis and machine intelligence, pp 679–698Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Peyman Sabouri
    • 1
  • Hamid GholamHosseini
    • 1
  • John Collins
    • 1
  1. 1.Department of Electrical and Electronics EngineeringAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations