Skip to main content

An Automated Approach Towards Digital Photo-Trichogram for Hair Fall Diagnosis

  • Conference paper
  • First Online:
Social Transformation – Digital Way (CSI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 836))

Included in the following conference series:

  • 841 Accesses

Abstract

Identification of a specific type of alopecia or hair loss is essential to get rid of hair loss issues. But identification of it is a challenging task to the medial experts. Among different techniques, Digital Photo-Trichogram is one of the popular non-invasive medical procedures for diagnosis of alopecia. In the present work we propose a novel system which is able to measure automatically the growth of hair without manual interaction and experts’ opinion. The developed system can estimate hair fall related issues with the help of parameters such as unit area density, approximate average height and width of hair, determination of vellus or terminal hair automatically from the picture of shaved region of alopecia effected area. The system is tested with the samples collected from Calcutta School of Tropical Medicine, Kolkata and achieve satisfactory results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. TrichoScan ……makes hair growth measurable - Home (n.d.). http://trichoscan.com/pages/english/home.php. Accessed 15 May 2014

  2. Saraogi, P.P., Dhurat, R.S.: Automated digital image analysis (TrichoScan®) for human hair growth analysis: ease versus errors. Int. J. Trichology 2(1), 5–13 (2010). https://doi.org/10.4103/0974-7753.66905

    Article  Google Scholar 

  3. Esfandiari, A., Kalantari, K.R., Babaei, A.: Hair loss diagnosis using artificial neural networks. Int. J. Comput. Sci. Issues 9(5(2)), 174–180 (2012)

    Google Scholar 

  4. Gassmueller, J., Rowold, E., Frase, T., Hughes-Formella, B.: Validation of TrichoScan® technology as a fully-automated tool for evaluation of hair growth parameters. Eur. J. Dermatol. 19(3), 224–231 (2009). https://doi.org/10.1684/ejd.2009.0640

    Article  Google Scholar 

  5. Basic Structure of Hair (n.d.). http://www.fbi.gov/

  6. Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000). https://doi.org/10.1016/S0031-3203(99)00055-2

    Article  Google Scholar 

  7. Mulinari-Brenner, F., Bergfeld, W.F.: Hair loss: diagnosis and management. Clevel. Clin. J. Med. (2003). https://doi.org/10.3949/ccjm.70.8.705

    Article  Google Scholar 

  8. Maru, D., Shah, B.: Image segmentation techniques and genetic algorithm. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 2(4), 1483–1487 (2013)

    Google Scholar 

  9. Dass, R., Devi, S.: Image segmentation techniques. Int. J. Electron. Commun. Technol. (IJECT) 3(1), 66–70 (2012)

    Google Scholar 

  10. Singh, T.R., Roy, S., Singh, O.I., Sinam, T., Singh, K.M.: A new local adaptive thresholding technique in binarization. Int. J. Comput. Sci. Issues 8(6), 271–277 (2012)

    Google Scholar 

Download references

Acknowledgement

Authors are thankful to the “Center for Microprocessor Application for Training Education and Research” of Computer Science & Engineering Department, Jadavpur University, for providing infrastructure facilities during progress of the work. Authors are also thankful to Dermatology department, Calcutta School of Tropical Medicine for providing useful data.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nibaran Das .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Debnath, N., Das, N., Sarkar, S., Nasipuri, M. (2018). An Automated Approach Towards Digital Photo-Trichogram for Hair Fall Diagnosis. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_28

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1343-1_28

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1342-4

  • Online ISBN: 978-981-13-1343-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics