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Techniques for the Detection of Skin Lesions in PH2 Dermoscopy Images Using Local Binary Pattern (LBP)

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1381))

Abstract

Skin lesion is the most deadly skin disease in humans, it arises as a result of disorders in the pigment cells, which is produced by a pigment known as melanin. This disease can be prevented and treated if there is an early diagnosis of the disease. Computer-Aided Diagnosis (CAD) has played a key role in helping dermatologists to diagnose the disease. In this proposed system, the model for diagnosis and classification of lesions consists of several stages beginning with pre-processing for the purpose of enhancing images, and identify the area of the lesion by isolating it from the healthy body, and extract features from an region of interest using the LBP method. Classification of the lesion into any of the three classes belong, benign or atypical or malignant according to database PH2. The results obtained for both SVM and K-NN classification techniques were 93.42% and 96.05% respectively.

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References

  1. Maton, A., et al.: Human Biology and Health. Prentice Hall, Englewood Cliffs, New Jersey (1893). ISBN 978-0-13-981176-0

    Google Scholar 

  2. Cakir, B.Ö., Adamson, P., Cingi, C.: Epidemiology and economic burden of nonmelanoma skin cancer. Facial Plast. Surg. Clin. North Am. 20(4), 419–422 (2012). https://doi.org/10.1016/j.fsc.2012.07.004. pmid 23084294

    Article  Google Scholar 

  3. Sajjad, R., Jerry, M. (eds.): ABC of skin cancer, pp. 5–6. Blackwell Pub, Malden (2008). ISBN 978-1-44-431250-8. Archived from the original on 29 April 2016

    Google Scholar 

  4. Dunphy, L.M.: Primary Care: The Art and Science of Advanced Practice Nursing. F.A. Davis. p. 242 (2011). ISBN 978-0-80-362647-8. Archived from the original on 20 May 2016

    Google Scholar 

  5. General Information About Melanoma. NCI. 17 April 2014. Archived from the original on 5 July 2014. Accessed 30 June 2014

    Google Scholar 

  6. Mahagaonkar, R.S., Soma, S.: A novel texture based skin melanoma detection using color GLCM and CS-LBP feature. Int. J. Comput. Appl. 975, 8887 (2015)

    Google Scholar 

  7. Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Contrast stretching-based unwanted artifacts removal from CT images. In: Santosh, K.C., Hegadi, R.S. (eds.) RTIP2R 2018. CCIS, vol. 1036, pp. 3–14. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9184-2_1

    Chapter  Google Scholar 

  8. Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Automated fractured bone segmentation and labeling from CT images. J. Med. Syst. 43(3), 1–13 (2019)

    Article  Google Scholar 

  9. Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Segmentation and analysis of CT images for bone fracture detection and labeling. In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques, vol. 131 (2019)

    Google Scholar 

  10. Santosh, K.C., Antani, S., Guru, D.S., Dey, N. (eds.): Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. CRC Press, Boca Raton (2019)

    Google Scholar 

  11. Santosh, K.C., Roy, P.P.: Arrow detection in biomedical images using sequential classifier. Int. J. Mach. Learn. Cybern. 9(6), 993–1006 (2016). https://doi.org/10.1007/s13042-016-0623-y

    Article  Google Scholar 

  12. Zohora, F.T., Antani, S., Santosh, K.: Circle-like foreign element detection in chest x-rays using normalized cross-correlation and unsupervised clustering. In: Medical Imaging 2018: Image Processing. vol. 10574, p. 105741V. International Society for Optics and Photonics (2018)

    Google Scholar 

  13. Jianu, Ş., Ichim, L., Popescu, D.: Advanced processing techniques for detection and classification of skin lesions. In: International Conference on System Theory, Control and Computing (ICSTCC). IEEE (2018)

    Google Scholar 

  14. Lopez, A.R., Giro-i-Nieto, X., Burdick, J., Marques, O.: Skin lesion classification from dermoscopic images using deep learning techniques. In: 2017 13th IASTED International Conference on Biomedical Engineering (BioMed), pp. 49–54. IEEE, February 2017

    Google Scholar 

  15. Fonseca-Pinto, R., Machado, M.: A textured scale-based approach to melanocytic skin lesions in dermoscopy. In: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 279–282. IEEE, May 2017

    Google Scholar 

  16. Sharma, R., Lal, M.: Skin cancer lesion classification using LBP based hybrid classifier. Int. J. Adv. Res. Comput. Sci. 8(7) (2017)

    Google Scholar 

  17. González-Castro, V., et al.: Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 1722–1726. IEEE, September 2015

    Google Scholar 

  18. Naeem, S., Riaz, F., Hassan, A., Nisar, R.: Description of visual content in dermoscopy ımages using joint histogram of multiresolution local binary patterns and local contrast. In: Jackowski, K., Burduk, R., Walkowiak, K., Woźniak, M., Yin, H. (eds.) IDEAL 2015. LNCS, vol. 9375, pp. 433–440. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24834-9_50

    Chapter  Google Scholar 

  19. Adjed, F., Faye, I., Ababsa, F., Gardezi, S.J., Dass, S.C.: Classification of skin cancer images using local binary pattern and SVM classifier. In: AIP Conference Proceedings, vol. 1787, no. 1, p. 080006. AIP Publishing, November 2016

    Google Scholar 

  20. Riaz, F., Hassan, A., Javed, M.Y., Coimbra, M.T.: Detecting melanoma in dermoscopy images using scale adaptive local binary patterns. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6758–6761. IEEE, August 2014

    Google Scholar 

  21. Hegadi, R.S., Navale, D.I., Pawar, T.D., Ruikar, D.D.: Multi feature-based classification of osteoarthritis in knee joint X-ray images.In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques, vol. 75 (2019)

    Google Scholar 

  22. Poornima, M.S., Shailaja, K.: Detection of skin cancer using SVM. Int. Res. J. Eng. Tech. (2017). e-ISSN 2395-0056. p-ISSN 2395

    Google Scholar 

  23. Laurent, C.D.: On active contour models and balloons. CVGIP Image Understand. 53, 211–218 (2004). https://doi.org/10.1016/1049-9660(91)90028-n

  24. Schaefer, G., Krawczyk, B., Celebi, M.E., Iyatomi, H.: An ensemble classification approach for melanoma diagnosis. Memetic Comput. 6(4), 233–240 (2014). https://doi.org/10.1007/s12293-014-0144-8

    Article  Google Scholar 

  25. Kasmi, R., Mokrani, K.: Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule. IET Image Process. 10(6), 448–455 (2016)

    Article  Google Scholar 

  26. Abuzaghleh, O., Barkana, B.D., Faezipour, M.: Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention. In: IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014, pp. 1–6. IEEE, May 2014

    Google Scholar 

  27. Barata, C., Ruela, M., Francisco, M., Mendonça, T., Marques, J.S.: Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 8(3), 965–979 (2013)

    Article  Google Scholar 

  28. Glaister, J., Amelard, R., Wong, A., Clausi, D.A.: MSIM: Multistage illumination modeling of dermatological photographs for illumination-corrected skin lesion analysis. IEEE Trans. Biomed. Eng. 60(7), 1873–1883 (2013)

    Article  Google Scholar 

  29. Barata, C., Celebi, M.E., Marques, J.S.: Improving dermoscopy image classification using color constancy. IEEE J. Biomed. Health İnf. 19(3), 1146–1152 (2014)

    Google Scholar 

  30. Mustafa, S., Kimura, A.: A SVM-based diagnosis of melanoma using only useful image features. In: 2018 International Workshop on Advanced Image Technology (IWAIT), pp. 1–4. IEEE, January 2018

    Google Scholar 

  31. Lee, H.D., et al.: Dermoscopic assisted diagnosis in melanoma: reviewing results, optimizing methodologies and quantifying empirical guidelines. Knowl.-Based Syst. 158, 9–24 (2018)

    Article  Google Scholar 

  32. Barata, C., Marques, J.S., Celebi, M.E.: Towards an automatic bag-of-features model for the classification of dermoscopy images: the influence of segmentation. In: 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 274–279. IEEE, September 2013

    Google Scholar 

  33. Cavalcanti, P.G., Scharcanski, J., Baranoski, G.V.: A two-stage approach for discriminating melanocytic skin lesions using standard cameras. Exp. Syst. Appl. 40(10), 4054–4064 (2013)

    Article  Google Scholar 

  34. Oliveira, R.B., Pereira, A.S., Tavares, J.M.R.S.: Computational diagnosis of skin lesions from dermoscopic images using combined features. Neural Comput. Appl. 31(10), 6091–6111 (2018). https://doi.org/10.1007/s00521-018-3439-8

    Article  Google Scholar 

  35. Riaz, F., et al.: Active contours based segmentation and lesion periphery analysis for characterization of skin lesions in dermoscopy images. IEEE J. Biomed. Health Inf. (2018)

    Google Scholar 

  36. Hegadi, R.S., Navale, D.I., Pawar, T.D., Ruikar, D.D.: Osteoarthritis detection and classification from knee x-ray images based on artificial neural network. In: Santosh, K.C., Hegadi, R.S. (eds.) RTIP2R 2018. CCIS, vol. 1036, pp. 97–105. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9184-2_8

    Chapter  Google Scholar 

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Acknowledgments

We thank the larger community of a hospital, Pedro Hispano, Matosinhos, Portugal (PH2) to provide this database and make it available to researchers. We thank all the collaborators for completing this paper.

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

    Ebrahim Mohammed Senan

  2. Marathwada Institute of Technology, Aurangabad, India

    Mukti E. Jadhav

Authors
  1. Ebrahim Mohammed Senan
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  2. Mukti E. Jadhav
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Corresponding authors

Correspondence to Ebrahim Mohammed Senan or Mukti E. Jadhav .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

    Bharti Gawali

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Senan, E.M., Jadhav, M.E. (2021). Techniques for the Detection of Skin Lesions in PH2 Dermoscopy Images Using Local Binary Pattern (LBP). In: Santosh, K.C., Gawali, B. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2020. Communications in Computer and Information Science, vol 1381. Springer, Singapore. https://doi.org/10.1007/978-981-16-0493-5_2

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  • DOI: https://doi.org/10.1007/978-981-16-0493-5_2

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  • Print ISBN: 978-981-16-0492-8

  • Online ISBN: 978-981-16-0493-5

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