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Two-Way Perceived Color Difference Saliency Algorithm for Image Segmentation of Port Wine Stains

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Intelligent Computing and Block Chain (FICC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1385))

Abstract

The image segmentation of port wine stains (PWS) lesions is of great significance to assess PDT treatment outcomes. However, it mainly depends on the manual division of doctors at present, which is time-consuming and laborious. Therefore, it is urgent and necessary to explore an efficient and accurate automatic extraction method for PWS lesion images. A two-way perceived color difference saliency algorithm (TPCS) for PWS lesion extraction is proposed to improve the efficiency and accuracy, and is compared with other image segmentation algorithms. The proposed algorithm shows the best performance with 88.91% accuracy and 96.36% sensitivity over 34 test images of PWS lesions.

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References

  1. Chen, J.K., Ghasri, P., Aguilar, G., Drooge, A.M.V., Wolkerstorfer, A., Kelly, K.M.: An overview of clinical and experimental treatment modalities for port wine stains. J. Am. Acad. Dermatol. 67(2), 289–304 (2012)

    Article  Google Scholar 

  2. Arnstadt, B., Ayvaz, A., Weingart, V., Wallner, J., Allescher, H.D., Bühren, V.: Port wine stain laser treatments and novel approaches. Facial Plast. Surg. 28(6), 611–620 (2012)

    Article  Google Scholar 

  3. Zhang, Y., Zou, X., Chen, H., Yang, Y., Lin, H., Guo, X.: Clinical study on clinical operation and post-treatment reactions of HMME-PDT in treatment of PWS. Photodiagn. Photodyn. Ther. 20(9), 253–256 (2017)

    Article  Google Scholar 

  4. Yuan, K.H., Gao, J.H., Huang, Z.: Adverse effects associated with photodynamic therapy (PDT) of port wine stain (PWS) birthmarks. Photodiagn. Photodyn. Ther. 9(4), 332–336 (2012)

    Article  Google Scholar 

  5. Gajanayake, G.M.N.R., Yapa, R.D., Hewawithana, B.: Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images. In: International Conference on Industrial & Information Systems, pp. 301–305. IEEE (2010)

    Google Scholar 

  6. Mubarak, D.M.N.: A hybrid region growing algorithm for medical image segmentation. Int. J. Comput. Inf. Technol. 4(3), 61–70 (2012)

    Google Scholar 

  7. Mao, J., Wang, K., Hu, Y., Sheng, W., Feng, Q.: GrabCut algorithm for dental X-ray images based on full threshold segmentation. IET Image Proc. 12(12), 2330–2335 (2018)

    Article  Google Scholar 

  8. Javed, R., Saba, T., Shafry, M., Rahim, M.: An intelligent saliency segmentation technique and classification of low contrast skin lesion dermoscopic images based on histogram decision. In: 12th International Conference on Developments in eSystems Engineering, pp. 164–169. IEEE (2019)

    Google Scholar 

  9. Vesal, S., Malakarjun Patil, S., Ravikumar, N., Maier, A.K.: A multi-task framework for skin lesion detection and segmentation. In: Stoyanov, D., et al. (eds.) CARE/CLIP/OR 2.0/ISIC -2018. LNCS, vol. 11041, pp. 285–293. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01201-4_31

    Chapter  Google Scholar 

  10. Ahn, E., Lei, B., Youn, H.J., Jinman, K.: Automated saliency-based lesion segmentation in dermoscopic images. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3009–3012. IEEE (2015)

    Google Scholar 

  11. Tang, X., Fan, X., Ying, L., Liu, W., Han, X.: Algorithm design of image segmentation in port wine stains photodynamic therapy binocular surveillance system. In: 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4. IEEE (2009)

    Google Scholar 

  12. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  13. Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Appl. Stats. 28(1), 100–108 (1979)

    Article  Google Scholar 

  14. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy C-means clustering algorithm. Comput. Geoences 10(2–3), 191–203 (1984)

    Google Scholar 

  15. Steven, W.Z.: Region growing: childhood and adolescence. Comput. Graph. Image Process. 5(3), 382–399 (1976)

    Article  Google Scholar 

  16. Rother, C.: GrabCut: interactive foreground extraction using iterated graph Cut. ACM Trans. Graph. 23(3), 309–314 (2004)

    Article  Google Scholar 

  17. Itti, L.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  18. Cheng, M., Zhang, G., Mitra, N.J., Huang, X., Hu, S.: Global contrast based salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 409–416. IEEE (2011)

    Google Scholar 

  19. Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: 14th ACM International Conference on Multimedia, New York, pp. 815–824. ACM (2006)

    Google Scholar 

  20. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, New York, pp. 1597–1604. IEEE (2009)

    Google Scholar 

  21. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.: Saliency detection via graph-based manifold ranking. In: IEEE Conference on Computer Vision and Pattern Recognition, New York, pp. 3166–3173. IEEE (2013)

    Google Scholar 

  22. Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.: Saliency detection via absorbing markov chain. In: IEEE International Conference on Computer Vision, New York, pp. 1665–1672. IEEE (2013)

    Google Scholar 

  23. Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: IEEE Conference on Computer Vision and Pattern Recognition, New York, pp. 2814–2821. IEEE (2014)

    Google Scholar 

  24. Margolin, R., Tal, A., Zelnik, M.L.: What makes a patch distinct?. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, New York, pp. 1139–1146. IEEE (2013)

    Google Scholar 

  25. Fairchild, M.D., Berns, R.S.: Image color-appearance specification through extension of CIELAB. Color Res. Appl. 18(3), 178–190 (1993)

    Article  Google Scholar 

  26. Lee, Y.K., Powers, J.M.: Comparisons of CIE lab, CIEDE 2000 and DIN 99 color differences between various shades of resin composites. Int. J. Prosthodont. 18(2), 150–155 (2005)

    Google Scholar 

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Acknowledgments

This research is supported by the National Natural Science Foundation of China (81773349).

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Authors and Affiliations

  1. School of Life Science, Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China

    Wenrui Kang, Xu Wang, Jixia Zhang, Xiaoming Hu & Qin Li

Authors
  1. Wenrui Kang
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  2. Xu Wang
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  3. Jixia Zhang
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  4. Xiaoming Hu
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  5. Qin Li
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Corresponding author

Correspondence to Xiaoming Hu .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Wanling Gao

  2. The Chinese University of Hong Kong, Shenzhen, China

    Kai Hwang

  3. Renmin University of China, Beijing, China

    Changyun Wang 

  4. Oklahoma State University, Stillwater, OK, USA

    Weiping Li

  5. Renmin University of China, Beijing, China

    Zhigang Qiu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Lei Wang

  7. East China Normal University, Shanghai, China

    Aoying Zhou

  8. East China Normal University, Shanghai, China

    Weining Qian

  9. East China Normal University, Shanghai, China

    Cheqing Jin

  10. Capital Medical University, Beijing, China

    Zhifei Zhang

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Kang, W., Wang, X., Zhang, J., Hu, X., Li, Q. (2021). Two-Way Perceived Color Difference Saliency Algorithm for Image Segmentation of Port Wine Stains. In: Gao, W., et al. Intelligent Computing and Block Chain. FICC 2020. Communications in Computer and Information Science, vol 1385. Springer, Singapore. https://doi.org/10.1007/978-981-16-1160-5_5

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  • DOI: https://doi.org/10.1007/978-981-16-1160-5_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1159-9

  • Online ISBN: 978-981-16-1160-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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