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Adaptive segmentation based on multi-classification model for dermoscopy images

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Abstract

Segmentation accuracy of dermoscopy images is important in the computer-aided diagnosis of skin cancer and a wide variety of segmentation methods for dermoscopy images have been developed. Considering that each method has its strengths and weaknesses, a novel adaptive segmentation framework based on multi-classification model is proposed for dermoscopy images. Firstly, five patterns of images are summarized according to the factors influencing segmentation. Then the matching relation is established between each image pattern and its optimal segmentationmethod. Next, the given image is classified into one of the five patterns by the multi-classification model based on BP neural network. Finally, the optimal segmentation method for this image is selected according to the matching relation, and then the image is effectively segmented. Experiments show that the proposed method delivers better accuracy and more robust segmentation results compared with the other seven state-of-the-art methods.

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Authors and Affiliations

  1. Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China

    Fengying Xie, Yefen Wu, Yang Li & Zhiguo Jiang

  2. General Hospital of the Air Force, People’s Liberation Army, Beijing, 100036, China

    Rusong Meng

Authors
  1. Fengying Xie
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  2. Yefen Wu
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  3. Yang Li
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  4. Zhiguo Jiang
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  5. Rusong Meng
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Corresponding author

Correspondence to Fengying Xie.

Additional information

Fengying Xie received the PhD in pattern recognition and intelligent system from Beihang University, China in 2009. She was a visiting scholar in the Laboratory for Image and Video Engineering (LIVE) at the University of Texas at Austin from 2010 to 2011. She is now an associate professor at the School of Astronautics in Beihang University. Her research interests include biomedical image processing, image quality assessment, image segmentation and classification.

Yefen Wu received the BS from the School of Electric& Electronic Engineering, Wuhan Polytechnic University, China in 2011. She is a postgraduate majoring in pattern recognition and intelligent system at School of Astronautics in Beihang University. Her research interests include biomedical image processing, segmentation and analysis.

Yang Li received the BS from the College of Information and Electrical Engineering, China Agricultural University, China in 2013. He is a postgraduate majoring in pattern recognition and intelligent system at School of astronautics in Beihang University. His research interests include biomedical image processing, segmentation and pattern recognition.

Zhiguo Jiang received the BS, MS and PhD from the Beihang University, China in 1987, 1990 and 2005, respectively. He is currently a professor in the Image Processing Center, Beihang University. His research interests include medical image processing, segmentation and classification, remotely sensed image processing, target detection, tracking and recognition.

Rusong Meng is a deputy chief physician of the General Hospital of the Air Force of PLA. His research interests are morphologic analysis of histiocyte, derma pathology and the clinic application of image analysis technology.

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Xie, F., Wu, Y., Li, Y. et al. Adaptive segmentation based on multi-classification model for dermoscopy images. Front. Comput. Sci. 9, 720–728 (2015). https://doi.org/10.1007/s11704-015-4391-8

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  • DOI: https://doi.org/10.1007/s11704-015-4391-8

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