Image Enhancement Based on Fractional Poisson for Segmentation of Skin Lesions Using the Watershed Transform

  • Alaa Ahmed Abbas Al-abayechi
  • Hamid A. JalabEmail author
  • Rabha W. Ibrahim
  • Ali M. Hasan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)


Image segmentation is considered as a necessary step towards accurate medical analysis by extracting the crucial medical information in identifying abnormalities. This study proposes a new technique for segmentation a malignant melanoma in images. A new filter is proposed for smoothing input images and more accurate segmentation based on fractional Poisson. In the pre-processing step, eight masks of size n × n are created to eliminate noise and obtain a smooth image. The watershed algorithm is used for segmentation with morphological operation to better segment the skin lesion area. The proposed method was capable of improving the accuracy of the segmentation up to 96.47%.


Fractional calculus Fractional Poisson Watershed algorithm Skin lesion Segmentation 



The authors would like to thank both Dr. Joaquim M. da Cunha Viana and Mr. Navid Razmjooy for providing the dermoscopic images used in this study. We would also like to thank skin specialist Dr. Mohammed Ahmed, for providing the necessary information for this study. This research is supported by the Fundamental Research Grant Scheme (FRGS), Project: FP073-2015A from Ministry of Higher Education, Malaysia.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alaa Ahmed Abbas Al-abayechi
    • 1
  • Hamid A. Jalab
    • 2
    Email author
  • Rabha W. Ibrahim
    • 3
  • Ali M. Hasan
    • 4
  1. 1.Al-Rusafa of Management InstituteMiddle Technical UniversityBaghdadIraq
  2. 2.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  3. 3.Modern College of Business and ScienceMuscatOman
  4. 4.School of Computing, Science and EngineeringUniversity of SalfordManchesterUK

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