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Automatic segmentation of dermoscopy images using saliency combined with adaptive thresholding based on wavelet transform

  • Kai HuEmail author
  • Si Liu
  • Yuan Zhang
  • Chunhong Cao
  • Fen Xiao
  • Wei Huang
  • Xieping Gao
Article
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Abstract

Segmentation is the essential requirement in automated computer-aided diagnosis (CAD) of skin diseases. In this paper, we propose an unsupervised skin lesion segmentation method to challenge the difficulties existing in the dermoscopy images such as low contrast, border indistinct, and skin lesion is close to the boundary. The proposed method combines the enhanced fusion saliency with adaptive thresholding based on wavelet transform to get the lesion regions. Firstly, a fusion saliency map increases the contract of the skin lesion and healthy skin, and then an adaptive thresholding method based on wavelet transform is used to obtain more accurate lesion regions. We compare the proposed method with seven state-of-the-art approaches using a series of evaluation metrics on both PH2 and ISBI2016 datasets. The results demonstrate the effectiveness of the proposed method superior to the state-of-the-art approaches in accordance with quantitative results and visual effects.

Keywords

Saliency map Adaptive thresholding Wavelet transform Dermoscopy images Segmentation 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61802328 and 61771415, and the Cernet Innovation Project under Grant NGII20170702.

References

  1. 1.
    Abuzaghleh O, Barkana BD, Faezipour M (2015) Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE J Translational Eng Health Med 3:1–12CrossRefGoogle Scholar
  2. 2.
    Ahn E, Bi L, Jung YH, Kim J, Li C, Fulham M, Feng DD (2015) Automated saliency-based lesion segmentation in dermoscopic images. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 3009–3012Google Scholar
  3. 3.
    Ahn E, Kim J, Bi L, Al Kumar, Li C, Fulham M, Feng DD (2017) Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J Biomed Health Inf 21(6):1685–1693CrossRefGoogle Scholar
  4. 4.
    Alcón JF, Ciuhu C, Ten Kate W, Heinrich A, Uzunbajakava N, Krekels G, Siem D, De Haan G (2009) Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE J Sel Top Sign Process 3(1):14–25CrossRefGoogle Scholar
  5. 5.
    Almasni MA, Alantari MA, Choi MT, Han SM, Kim TS (2018) Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput Methods Prog Biomed 162:221–231CrossRefGoogle Scholar
  6. 6.
    Basalamah S (2012) Histogram based circle detection. Int J Comput Sci Netw Secur 12(8):40–43Google Scholar
  7. 7.
    Bernard WS, Christopher PW (2014) World cancer report 2014. World Health OrganizationGoogle Scholar
  8. 8.
    Borji A, Frintrop S, Sihite DN, Itti L (2012) Adaptive object tracking by learning background context. In: IEEE computer society conference on computer vision and pattern recognition workshops, pp 23–30Google Scholar
  9. 9.
    Chen X, Li Q, Song Y, Jin X, Zhao Q (2012) Supervised geodesic propagation for semantic label transfer. In: European conference on computer vision, pp 553–565Google Scholar
  10. 10.
    Cheng MM, Mitra NJ, Huang X, Torr P HS, Hu SM (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582CrossRefGoogle Scholar
  11. 11.
    Emre Celebi M, Kingravi HA, Iyatomi H, Alp Aslandogan Y, Stoecker WV (2008) Border detection in dermoscopy images using statistical region merging. Skin Res Technol 14(3):347–353CrossRefGoogle Scholar
  12. 12.
    Fan H, Xie F, Li Y, Jiang Z, Liu J (2017) Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold. Comput Biol Med 85:75–85CrossRefGoogle Scholar
  13. 13.
    Garnavi R, Aldeen M, Celebi ME, Varigos G, Finch S (2011) Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Comput Med Imaging Graph 35(2):105–115CrossRefGoogle Scholar
  14. 14.
    Guo M, Zhao Y, Zhang C, Chen Z (2014) Fast object detection based on selective visual attention. Neurocomputing 144:184–197CrossRefGoogle Scholar
  15. 15.
    Gutman D, Codella NCF, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv:1605.01397
  16. 16.
    Hu K, Gao X, Li F (2011) Detection of suspicious lesions by adaptive thresholding based on multiresolution analysis in mammograms. IEEE Trans Instrum Meas 60(2):462–472CrossRefGoogle Scholar
  17. 17.
    Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, Gao X (2018) Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309:179–191Google Scholar
  18. 18.
    Huang LK, Wang MJ J (1995) Image thresholding by minimizing the measures of fuzziness. Pattern Recogn 28(1):41–51CrossRefGoogle Scholar
  19. 19.
    Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259CrossRefGoogle Scholar
  20. 20.
    Jahanifar M, Tajeddin NZ, Asl BM, Gooya A (2018) Supervised saliency map driven segmentation of lesions in dermoscopic images. IEEE J Biomed Health Inf 1–1.  https://doi.org/10.1109/JBHI.2018.2839647
  21. 21.
    Jin X, Sun X, Zhang X, Sun H, Xu R, Zhou X, Li X, Liu R (2018) Sun orientation estimation from a single image using short-cuts in DCNN. Opt Laser Technol 110:191–195Google Scholar
  22. 22.
    Kasmi R, Mokrani K, Rader RK, Cole JG, Stoecker WV (2016) Biologically inspired skin lesion segmentation using a geodesic active contour technique. Skin Res Technol 22(2):208–222CrossRefGoogle Scholar
  23. 23.
    Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recogn 19(1):41–47CrossRefGoogle Scholar
  24. 24.
    Lee T, Ng V, Gallagher R, Coldman A, McLean D (1997) Dullrazor®;: a software approach to hair removal from images. Comput Biol Med 27(6):533–543CrossRefGoogle Scholar
  25. 25.
    Li Q, Chen X, Song Y, Zhang Y, Jin X, Zhao Q (2014) Geodesic propagation for semantic labeling. IEEE Trans Image Process 23(11):4812–4825MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Li C, Yuan Y, Cai W, Xia Y, Dagan Feng D (2015) Robust saliency detection via regularized random walks ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2710–2717Google Scholar
  27. 27.
    Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak J AWM, Van Bram G, Sánchez C I (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88CrossRefGoogle Scholar
  28. 28.
    Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L, Li X, Li J, Serikawa S (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comput: Pract Exp 29(6):e3927CrossRefGoogle Scholar
  29. 29.
    Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mobile Netw Appl 23(2):368–375CrossRefGoogle Scholar
  30. 30.
    Lu H, Li Y, Uemura T, Kim H, Serikawa S (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur Gener Comput Syst 82:142–148Google Scholar
  31. 31.
    Mendonça T, Ferreira PM, Marques JS, Marcal A RS, Rozeira J (2013) PH 2-A dermoscopic image database for research and benchmarking. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 5437–5440Google Scholar
  32. 32.
    Navarro F, Escudero-Vinolo M, Bescos J (2018) Accurate segmentation and registration of skin lesion images to evaluate lesion change. IEEE J Biomed Health Inf 1–1.  https://doi.org/10.1109/JBHI.2018.2825251
  33. 33.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRefGoogle Scholar
  34. 34.
    Pathan S, Prabhu KG, Siddalingaswamy PC (2018) Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—a review. Biomed Signal Process Control 39:237–262CrossRefGoogle Scholar
  35. 35.
    Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–166CrossRefGoogle Scholar
  36. 36.
    Silveira M, Nascimento JC, Marques JS, Marçal ARS, Mendonça T, Yamauchi S, Maeda J, Rozeira J (2009) Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J Sel Top Sign Process 3(1):35–45CrossRefGoogle Scholar
  37. 37.
    Wang L, Adeli E, Wang Q, Shi Y, Suk HI (2016) Machine learning in medical imaging. In: 7th International workshop, MLMI 2016. Held in conjunction with MICCAI 2016, vol 10019Google Scholar
  38. 38.
    Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1155–1162Google Scholar
  39. 39.
    Yang C, Zhang L, Lu H, Ruan X, Yang MH (2013) Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3166–3173Google Scholar
  40. 40.
    Yang X, Liu C, Wang Z, Yang J, Le Min H, Wang L, Cheng KT T (2017) Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI. Med Image Anal 42:212–227CrossRefGoogle Scholar
  41. 41.
    Yüksel ME, Borlu M (2009) Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic. IEEE Trans Fuzzy Syst 17(4):976–982CrossRefGoogle Scholar
  42. 42.
    Zeng B, Huang Q, El Saddik A, Li H, Jiang S, Fan X (2018) Advances in multimedia information processing-PCM 2017. In: 18th Pacific-rim conference on multimedia, vol 10736Google Scholar
  43. 43.
    Zhang XP, Desai MD (2001) Segmentation of bright targets using wavelets and adaptive thresholding. IEEE Trans Image Process 10(7):1020–1030zbMATHCrossRefGoogle Scholar
  44. 44.
    Zhang Y, Gravina R, Lu H, Villari M, Fortino G (2018) PEA: parallel electrocardiogram-based authentication for smart healthcare systems. J Netw Comput Appl 117:10–16CrossRefGoogle Scholar
  45. 45.
    Zhao Y, Zheng Y, Liu Y, Yang J, Zhao Y, Chen D, Wang Y (2017) Intensity and compactness enabled saliency estimation for leakage detection in diabetic and malarial retinopathy. IEEE Trans Med Imaging 36(1):51–63CrossRefGoogle Scholar
  46. 46.
    Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2814–2821Google Scholar
  47. 47.
    Zortea M, Flores E, Scharcanski J (2017) A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images. Pattern Recogn 64:92–104CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan UniversityXiangtanChina
  2. 2.Postdoctoral Research Station for MechanicsXiangtan UniversityXiangtanChina
  3. 3.Department of RadiologyThe First Hospital of ChangshaChangshaChina

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