Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Texture Based Automated Segmentation of Skin Lesions using Echo State Neural Networks

  • Khan, Z. Faizal (Dept. of Computer and Network Engineering, College of Engineering, Shaqra University) ;
  • Ganapathi, Nalinipriya (Department of Information Technology, Saveetha Engineering College)
  • Received : 2015.07.15
  • Accepted : 2016.10.07
  • Published : 2017.01.02
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Abstract

A novel method of Skin lesion segmentation based on the combination of Texture and Neural Network is proposed in this paper. This paper combines the textures of different pixels in the skin images in order to increase the performance of lesion segmentation. For segmenting skin lesions, a two-step process is done. First, automatic border detection is performed to separate the lesion from the background skin. This begins by identifying the features that represent the lesion border clearly by the process of Texture analysis. In the second step, the obtained features are given as input towards the Recurrent Echo state neural networks in order to obtain the segmented skin lesion region. The proposed algorithm is trained and tested for 862 skin lesion images in order to evaluate the accuracy of segmentation. Overall accuracy of the proposed method is compared with existing algorithms. An average accuracy of 98.8% for segmenting skin lesion images has been obtained.

Keywords

References

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