Collagen second harmonic generation image analysis for diabetes determination

A collagen second harmonic generation (SHG) and third harmonic generation (THG) image analysis algorithm based on feature extraction and classification for diabetes disease determination is developed in the paper. It is designed to find the early symptoms of diabetes by using the proposed method to calculate diabetes estimation indicator. The algorithm detects the basic diabetes featured elements on images of human dermis skin. The proposed algorithm consists of the processes of smoothing, adaptive three-region thresholding, binarization, segmentation, feature extraction, and diabetes disease determination. This feature classification criterion provides a new viewpoint to help diabetes diagnosis.

[1]  P. Hemant,et al.  A novel approach to predict diabetes by Cascading Clustering and Classification , 2012, 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12).

[2]  Gwo Giun Lee,et al.  Cell segmentation and NC ratio analysis of third harmonic generation virtual biopsy images based on marker-controlled gradient watershed algorithm , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[3]  Hung T. Nguyen,et al.  Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks , 2010, IEEE Congress on Evolutionary Computation.

[4]  Chang-Shing Lee,et al.  Ontology-based Fuzzy Inference Agent for Diabetes Classification , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[5]  R. Sammouda,et al.  Identification of Lung Cancer Based on Shape and Color , 2007, 2007 Innovations in Information Technologies (IIT).