Early detection of diabetes using image processing with aid of iridology by P.H.A.H.K.Yashodhara Reg. No EEY6D95 Individual Project – Type A (Second Progress Presentation) Supervisor Dr.D.D.M. Ranasinghe

Content Introduction Theoretical background Methodology Discussion Future work References 2

Introduction World Health Organization – Diabetes country profiles,

Diabetes Diabetes, also formally known as diabetes mellitus - group of metabolic diseases. With diabetes, the affected individual has high blood glucose (or blood sugar) due to one or both of the following reasons: –Insulin production is inadequate –Body’s cells do not properly respond to the insulin. 4

Identification of diabetes through Pancreas 5

Why Iridology? Random blood sugar test. Fasting blood sugar test. Oral glucose tolerance test. 6

Aim and Objectives Aim –Introduce noninvasive, automated and accurate alternative medicine technique to early detect diabetes. Objectives –Learn the concepts, methods and techniques of the alternative medicine technique iridology. –Study the changes in the features of iris with respect to diabetes. –Design and implement an algorithm for iris recognition and develop a system. –Evaluate the developed system against benchmark dataset. –Apply the evaluated system for local data set to predict diabetes. 7

Iridology An alternative medicine technique 8

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Iridology 10

Anatomy of the human eye 11

Methodology Image Acquisition Image Pre-processing Noise reduction Median filter Mean filter Wiener filter Enhancement Histogram Equalization Iris Localization/ Segmentation Daugman’s operator Normalization Daughman’s rubber sheet model Feature Extraction ROI extraction Using iridology chart Principal Component Analysis Gabor Wavelet Transform Classification ANN, SVM 12

Processing Stages Pre-processing stage –Image acquisition Iridology camera/Free databases(eg:MMU) TypeSize Mother has50 Father has50 Both50 Non50 Iridology Camera Data set 13

Processing Stages Processing stage –Filtering Median, Mean, Wiener, Unsharp mask –Localization/segmentation Daugman’s integrodifferential operator –Normalization Daugman’s Rubber Sheet Model –Enhancement Contrast Limited Adaptive Histogram Equalization 14

Filtering Original imageMedian filter image Wiener filter image Sharpened image Mean filter image 15

Localization/segmentation Daugman’s integro-differential operator 16

Localization/segmentation Daugman’s integro-differential operator Daughman circle detection of iris separating iris from sclera zone and pupil 17

Localization/segmentation Daugman’s integro-differential operator Segmented iris image 18

Normalization Daughman’s rubber sheet model I{ x(r, θ), y(r, θ) }I(r, θ) x(r, θ) = (1 - r) x p (θ) + rx l (θ) y(r, θ) = (1 - r) y p (θ) + ry l (θ) 19

Normalization Daughman’s rubber sheet model Normalized iris image 20

Enhancement Histogram equalization Enhanced iris image 21

Processing Stages Post Processing stage –Feature extraction ROI extraction 01: :15 for right eye 07:15 – 07:45 for left eye Principal Component Analysis Gabor Wavelet Transform –Classification Artificial Neural Network Support Vector Machine 22

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Left eye of iridology chart by Jensen Bernard 24

Iris signs of Diabetic Patient Small fine, blood vessels develop on the anterior surface Signs of diabetes 25

Iris signs of Diabetic Patient Lymphatic iris with orange pigmentations 26

User Interface 27

User Interface 28

Advantages and Limitations AdvantagesLimitations Non-invasive and safe Cost effective Iris signs manifest before gross pathology does, thus iridology may provide information on vital processes before symptoms manifest - therefore it is particularly useful in preventative care It provides a valuable framework for assessing future limitations and potentials of a patient’s health Cannot identify types of diabetes 29

Discussion Identification of the organ Pancreas as a suitable organ to detect diabetes through iridology. Part of methodology is implemented. Need further resting with the data set for improving the precision. 30

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Future work Purchasing of a camera. Playing, testing with local data set.

References [1] A. D. Wibawa and M. H. Purnomo, “Early detection on the condition of pancreas organ as the cause of diabetes mellitus by real time iris image processing,” in Proc. IEEE Asia Pacific Conference on Circuits and Systems, 2006, pp [2] S.B. More and Prof. N. D Pergad, “On a Methodology for Detecting Diabetic Presence from Iris Image Analysis,” in International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering, (An ISO 3297: 2007 Certified Organization), Vol. 4, Issue 6, June [3] A. Bansal, R. Agarwal, and R. K. Sharma, “Determining diabetes using iris recognition system,” Int. J Diabetes Dev Ctries, vol. 34, no. 4, pp , [4] J. F. Banzi and Z. Xue, “An Automated Tool for Non-contact, Real Time Early Detection of Diabetes by Computer Vision,” Int. J. Mach. Learn. Comput., vol. 5, no. 3, pp. 225–229,

Thank you! 34