ارتباط بیماری دیابت و اعتیاد با اثر انگشت

 Background: Human skin more than any other part of the body, is exposed to the risks of diseases and complications of labor. One of the applications of study on the relationship between skin and diseases is use of fingerprints in the diagnosis and the subsequent treatment of it. We analyzed the fingerprint images of two systematic diseases namely diabetes and addiction. Methods: The first method has been used in the data analysis was power spectrum. The results showed that in order to extract the features from fingerprint images other methods must be found. The combination of textural features extracted from the wavelet coefficients with the statistical features of wavelet, will make stronger feature vector. In this thesis, two methods based on statistical characteristics of wavelet and texture features of images have been used for analysis of fingerprint images in patients. Results: Wavelet transform and extracted features from wavelet coefficients act stronger than the Fourier transform in image analysis. Combination of wavelet and texture features had the best results. Results of addiction and diabetes test were 73% and 67% respectively. Conclusions: These results are promising in detecting relationship between fingerprints with diseases. More research is needed on this topic.