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November Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the November 2021 Edition of the journal. The last date of research paper submission is October 15, 2021.

Medical Image Analysis System for Segmenting Skin Diseases using Digital Image Processing Technology

Tanjila Broti, Anika Siddika, Sikdar Rituparna, Nadia Hossain, Nazmus Sakib in Image Processing

International Journal of Applied Information Systems
Year of Publication:2020
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Tanjila Broti, Anika Siddika, Sikdar Rituparna, Nadia Hossain, Nazmus Sakib
10.5120/ijais2020451849
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  1. Tanjila Broti, Anika Siddika, Sikdar Rituparna, Nadia Hossain and Nazmus Sakib. Medical Image Analysis System for Segmenting Skin Diseases using Digital Image Processing Technology. International Journal of Applied Information Systems 12(28):7-15, March 2020. URL, DOI BibTeX

    @article{10.5120/ijais2020451849,
    	author = "Tanjila Broti and Anika Siddika and Sikdar Rituparna and Nadia Hossain and Nazmus Sakib",
    	title = "Medical Image Analysis System for Segmenting Skin Diseases using Digital Image Processing Technology",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "March 2020",
    	volume = 12,
    	number = 28,
    	month = "March",
    	year = 2020,
    	issn = "2249-0868",
    	pages = "7-15",
    	url = "http://www.ijais.org/archives/volume12/number28/1080-2020451849",
    	doi = "10.5120/ijais2020451849",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

Digital Image Processing (DIP) provisions robust research platform in areas of epidermis, dermis, and subcutaneous tissues. The skin is the principal organ of the human body, containing blood vessels, lymphatic vessels, nerves, and muscles, which can perspire, perceive the external temperature, and protect the body which can be faced larger problem directed by any skin disease. This research deals with various image processing techniques, image segmentation shows a vital role in step to analyze the given image and has become a prominent objective in computer vision. This work deals on the basic principles on the methods used to segment the infected part in an image and pre-processing of images to enhance the quality on the four diseases namely: Seborrheic Dermatitis, Diabetic Foot Ulcer, Impetigo, and Melanoma. Here, three segmentation methods for the given four diseases are evaluated for the efficient use for the medical purpose.

Reference

  1. L.-s. Wei, Q. Gan, and T. Ji, “Skin disease recognition method based on image color and texture features,” Computational and mathematical methods in medicine, vol. 2018, 2018.
  2. N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler, et al., “Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic),” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172, IEEE, 2018.
  3. L. Bajaj, H. Kumar, and Y. Hasija, “Automated system for prediction of skin disease using image processing and machine learning,” International Journal of Computer Applications, vol. 975, p. 8887.
  4. P. S. Ambad and A. Shirsat, “A image analysis system to detect skin diseases,” IOSR Journal of VLSI and Signal Processing, vol. 6, no. 5, pp. 17–25, 2016.
  5. A. Ajith, V. Goel, P. Vazirani, and M. M. Roja, “Digital dermatology: Skin disease detection model using image processing,” in 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 168–173, IEEE, 2017
  6. M. N. Islam, J. Gallardo-Alvarado, M. Abu, N. A. Salman, S. P. Rengan, and S. Said, “Skin disease recognition using texture analysis,” in 2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC), pp. 144–148, IEEE, 2017.
  7. P. R. Hegde, M. M. Shenoy, and B. Shekar, “Comparison of machine learning algorithms for skin disease classification using color and texture features,” in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1825–1828, IEEE, 2018.
  8. E H. Page, MD, Assistant Clinical Professor of Dermatology; Physician, Harvard Medical School; Lahey Hospital and Medical Center "Diagnosis of skin disorders", MSD Manual Consumer Version, 2017. [Online]. Available: http://www.merckmanuals.com/home/skindisorders/biology-of-the-skin/diagnosis-of-skin-disorders. [Accessed: 07- Oct-2016].
  9. D. A. Okuboyejo, O. O. Olugbara, and S. A. Odunaike, “Automating skin disease diagnosis using image classification,” in proceedings of the world congress on engineering and computer science, vol. 2, pp. 850–854, 2013.
  10. V. B. Kumar, S. S. Kumar, and V. Saboo, “Dermatological disease detection using image processing and machine learning,” in 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR), pp. 1–6, IEEE, 2016.
  11. M. Kumar and R. kumar, "AN INTELLIGENT SYSTEM TO DIAGNOSIS THE SKIN DISEASE," ARPN Journal of Engineering and Applied Sciences, vol. 11, 2016.
  12. Withana, Uvin; Fernando, Pumudu, "Differential diagnosis of eczema and psoriasis using categorical data in image processing," Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer), 2017.
  13. M. S. Arifin, M. G. Kibria, A. Firoze, M. A. Amini and H. Yan, "Dermatological disease diagnosis using color-skin images," International Conference on Machine Learning and Cybernetics, 2012.
  14. Y. Hasija, N. Garg and S. Sourav, "Automated detection of dermatological disorders through image-processing and machine learning," International Conference on Intelligent Sustainable Systems (ICISS), 2017
  15. A. G. Priya.H, J. Anitha and J. Poonima.J, "Identification of Melanoma in Dermoscopy Images Using Image Processing Algorithms," International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), 2018.
  16. R. Suganya, "An automated computer aided diagnosis of skin lesions detection and classification for dermoscopy images," International Conference on Recent Trends in Information Technology (ICRTIT), 2016
  17. N. J. Dhinagar and M. Celenk, "Early diagnosis and predictive monitoring of skin diseases," IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT), 2016
  18. S. Mane and S. Shinde, "A Method for Melanoma Skin Cancer Detection Using Dermoscopy Images," Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018.
  19. M. R. Tabassum, A. U. Gias, M. Kamal, H. M Muctadir, M. Ibrahim, A. K. Shakir, and M. Islam, "Comparative study of statistical skin detection algorithms for sub-continental human images". Information Technology Journal, vol. 9, issue. 4, pp.811-817, 2010.
  20. Y. T. Hsiao, C. L. Chuang, J. A. Jiang, and C. C. Chien, "A contour-based image segmentation algorithm using morphological edge detection". In Proc. of IEEE International Conference, Systems, Man and Cybernetics, vol. 3, pp. 2962-2967, 2005.
  21. O. Trabelsi, L. Tlig, M. Sayadi and F. Fnaiech, "Skin disease analysis and tracking based on image segmentation," International Conference on Electrical Engineering and Software Applications, 2013.
  22. R. S. Gound, Priyanka S. Gadre, Jyoti B. Gaikwad, Priyanka K. Wagh, ‘’ Skin Disease Diagnosis System using Image Processing and Data Mining’’. International Journal of Computer Applications, Volume 179 – No.16, January 2018
  23. Firas Ajil Jassim, Fawzi H. Altaani, ‘’Hybridization of Otsu Method and Median Filter for Color Image Segmentation’’. International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-2, May 2013
  24. Mrs. S.Kalaiarasi, Harsh Kumar, Sourav Patra, “Dermatological Disease Detection using Image Processing and Neural Networks”. International Journal of Computer Science and Mobile Applications, Vol.6 Issue. 4, April- 2018.
  25. “Differences of seborrheic dermatitis and impetigo.” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2642512. Accessed: 2019-12-29.
  26. “Differences of seborrheic dermatitis and melanoma.” https://www.medicalnewstoday.com/articles/320742.php. Accessed: 2019-12-29.
  27. “Differences of diabetic ulcer and melanoma.” https://www.mayoclinic.org/diseasesconditions/melanoma/symptoms-causes/syc-20374884. Accessed: 2019-12-29.
  28. “Differences of diabetic ulcer and impetigo.” https://www.nhsinform.scot/illnesses-and conditions/infections-and-poisoning/impetigo.Accessed: 2019-12-29.
  29. “Differences of diabetic ulcer and impetigo.” https://emedicine.medscape.com/article/460282overview. Accessed: 2019-12-29.
  30. “Image pre-processing.” https://towardsdatascience.com/image-pre-processing-c1aec0be3edf. Accessed: 2019-12-29.
  31. M. Messadi, A. Bessaid, and A. Taleb-Ahmed, “Extraction of specific parameters for skin tumor classification,” Journal of medical engineering & technology, vol. 33, no. 4, pp. 288–295, 2009.

Keywords

Skin disease, Segmentation, K-Means, Marker-controlled Watershed, Otsu thresholding, Jaccard Index, Dice Coefficient