Sa-Oiliness: A System to Measure Oiliness of Distant Subject Using Image Processing

Abstract:

In medical examination of skin texture, an expert is generally needed to examine the skin texture thoroughly. It is felt that the same can be achieved through image processing, wherein a skin expert or physical presence of the patient are not needed on the spot. In the distant skin examination, expertise is required in analyzing the skin texture as appeared on the image. The texture is evaluated on the basis of oiliness of the skin, which is measured by a specific instrument [5]. As per our knowledge, the instrument is not readily available. To overcome this problem, a framework (SA-OILINESS) is proposed in this paper. It is designed so as to provide a scale to measure oiliness of facial skin through an image of a standard resolution. The proposed approach is based on the principle that the intensity of light from an oily skin is comparatively higher than that from a dry skin. Images of test subjects are taken at the same time and using the approach of weighted mean of the intensity of light reflected from the marked forehead region a scale of oiliness is developed. The scale is used to measure the relative value of oiliness.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 4462-4468
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.4462
• Online since: November 2011
• Authors: Anand Gupta, Rachit Garg, Shruti Suri
• Keywords: Est Image, Gray Scale Image, Histogram, Oiliness, Weighted Mean

References

[1] A. Sparavigna and R. Marazzato, An image processing analysis of skin textures, Wiley Journal on Skin Research and Technology, vol. 16, Issue 2, pp.161-167, May (2010).

DOI: 10.1111/j.1600-0846.2009.00413.x

[2] M. Doi and S.  Tominaga, Image Analysis and Synthesis of Skin Color Textures by Wavelet Transform, IEEE Southwest Symposium on Image Analysis and Interpretation, pp.193-193, Denver, Colorado March 26-28, (2006).

DOI: 10.1109/ssiai.2006.1633749

[3] S. L. Phung, D.  Chai and A.  Bouzerdoum, Adaptive skin segmentation in color images", International Conference on Acoustics, Speech, and Signal Processing (ICASSP , 03), pp.353-356, Hong Kong, April (2003).

DOI: 10.1109/icassp.2003.1199483

[4] M. Nishioka,  M. Fukumi, N. Akamatsu and Y.  Mitsukura, Measurement of skin texture using genetic image analysis", International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS , 04), pp.787-791, Seoul, Korea, 18-19 Nov. (2004).

DOI: 10.1109/ispacs.2004.1439168

[5] J. Lee, Y. Chen, L. Liu and C. Huang, A Facial-Skin Condition Classification System Using Texture and Fuzzy C-Means Technologies", Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD , 07), pp.127-131, Haikou, Hainan, China, 24-27 Aug. (2007).

DOI: 10.1109/fskd.2007.22

[6] R.M. Haralick, K. Shanmugam, and I. Dinstein, Texture Features for Image Classification, IEEE Trans. Systems, Man, and Cybernetics vol. 3, no. 6, pp.610-621, (1973).

DOI: 10.1109/tsmc.1973.4309314

[7] O. G . Gula, K. J. Dona, F. P. Murphy, B. K. Rao, Bidirectional Imaging and Modeling of Skin Texture, IEEE Trans. On Biomedical Engineering, vol. 51, no. 12, pp.2148-2159, (2004).

DOI: 10.1109/tbme.2004.836520

[8] H. H. Huang, Research on the Features of Human Skin Appearance by Image Processing, Thesis, Dept. of Information Management, Yuan-Ze University, Taiwan, (2006).

[9] VSO Image Resizer, Available at http: /www. vso-software. fr/products/image_resizer.

[10] Image Cut Software, Available at http: /www. sliceimage. com.

[11] Matlab Software, Available at http: /www. mathworks. com/products/matlab/tryit. html.