A Hybrid Computational Intelligence Algorithm for Automatic Skin Lesion Segmentation in Dermoscopy Images


In this paper, an unsupervised approach based on evolving vector quantization (EVQ) is presented for enhancing dermatology images for skin lesion segmentation. Vector quantization (VQ) as a famous compression technique has been widely used in image signal compression and speech signal compression. The EVQ algorithm extends the Linde, Buzo, and Gray (LBG) vector quantization method with particle swarm optimization to cluster the pixels inside the image based on merging similar gray value pixels. The proposed enhancement technique is evaluated using 100 dermoscopy skin lesion images for skin lesion segmentation. The EVQ algorithm is applied to the individual color planes, red, green, and blue, respectively. Segmentation results using these three planes are compared and scored based on manual borders obtained from three dermatologists. In addition, differential equation-based particle swarm optimization is implemented and their results are compared with the standard PSO.

  • Abstract
  • Keywords
  • 1. Introduction
  • 2. Preprocessing
  • 3. Evolving Vector Quantization
  • 4. Postprocessing
  • 5. Results and Discussion
  • 6. Conclusion and Future Work
  • 7. References

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