This document proposes a noninvasive method to detect diabetes mellitus using facial images. It extracts texture features using a Gabor filter bank and color features in the Lab color space from divided facial blocks. These features are classified using k-NN and SVM algorithms. Experimental results show classification accuracies up to 94.28% for k-NN and 97.14% for SVM, providing an alternative to invasive blood glucose tests. The method aims to detect differences in facial skin associated with diabetes status based on traditional Chinese medicine hypotheses.

1. NONINVASIVE DIABETES
MELLITUS DETECTION
MD TASNIM
M.TECH
INSTRUMENTATION & CONTROL
16EEIM034
GB2822
SERIAL NO-08

2. INTRODUCTION TO DIABETES
MELLITUS
• type1 diabetes-lack of insulin
• Type2 diabetes-affects the way the body
processes glucose
• Prediabetes-A condition in which blood sugar
is high, but not high enough to be type 2
diabetes
• Gestational diabetes-A form of high blood
sugar affecting pregnant women.

3. Symptoms of type 1 and type 2
diabetes include
• increased urine output
• excessive thirst
• weight loss
• hunger
• fatigue
• skin problems
• slow healing wounds
• yeast infections and
• tingling or numbness in the feet or toes
• Over time, diabetes can lead to blindness, kidney
failure, and nerve damage

4. Conventional method
• fasting plasma glucose (FPG) test
• For this test, patient has to check the blood
after fasting for 12hours
• requires a drop of patient’s blood by
puncturing their finger for analysing glucose
level in the blood
• precise but it is invasive, time consuming and
quite painful

5. PROPOSED METHODOLOGY

6. STEPS IN THIS METHODOLOGY
• To begin with, 2-D GF bank is created.
• Second, texture value of each block calculated using
GF bank There is one value for every one filter in the
filter bank. The mean of all the values of filters in GF
bank is nothing but the texture value of facial block
• Third, extraction of color feature vector from facial
block is done.
• For that Lab color space transformation is performed
After extracting the facial block texture and color
features, k-NN and SVM are applied to differentiate
DM verses Healthy classes.

7. TEXTURE FEATURE EXTRACTION

8. A. Pre- processing of Facial Image
• Facial images are collected from hospital
• an accurate way a color correction procedure
is performed.
• For color correction, individual red, green and
blue channels of RGB image are extracted
• The mean of each channel is calculated
• Correction factor of each channel is
calculated using desired mean and mean of
each channel.

9. • Desired mean = mean (mean R, mean G, mean
B).
• Corrected factor = desired mean / mean of
individual channel. Corrected
• individual channel = individual channel *
correction factor of that channel.
• By linking each corrected channel we get
color corrected RGB image

10. • In Traditional Chinese Medicine (TCM)
hypothesis [8, 9],
• it is explained that distinct regions of the face
can reflect
• (physical condition status) the reason,
symptoms and source of
• the disease.

12. B. Extraction of Facial Texture Features
• To calculate the texture value of each block a
2-D GF is applied and defined as
• Where x’= x.cos θ + y.sin θ, y’= -x.sin θ + y.cos
θ, σ is the
• variance, λ is the wavelength, γ is the aspect
ratio of the
• sinusoidal function, and θ is the orientation.
For getting better
• results, five σ (variance) values (one to five)

13. COLOR FEATURE EXTRACTION AND
CLASSIFICATION
• COLOR FEATURE EXTRACTION AND
CLASSIFICATION
• After the color correction procedure and block
extraction
• from input facial image, color space
transformation is performed.

14. Fig. 6 shows the example of color block
samples after color correction procedure.

15. • RGB to Lab color space transformation is
performed.
• Since RGB is not absolute color
space .
• For this transformation two step are
required.
• First, convert RGB to XYZ color space and then
convert XYZ to lab color space.

16. • In L*a*b color space,
• L- luminosity layer,
• a-chromaticity layer representing that color falls
along the red-green axis
• b- chromaticity layer representing that color falls
along the blue-yellow axis
• It is the most absolute color model utilized
usually to illustrate all the colors visible to the
human eye

17. • To extract the color feature vector, first read
input color
• image that uses the sRGB color space, second
perform a color
• transformation that defines an RGB to L*a*b*
conversion
• using MATLAB term makecform('srgb2lab').
And then perform the transformation with
applycform to get the L*a*b* values, mean of
L*a*b* values is the color feature value of one
block.

18. B. Classification
Once the facial color feature and texture feature vectors
are extracted from the facial blocks, they are classified
utilizing the k-NN and SVM classifiers.
• each facial block is represented by v. This implies one
facial image sample S has a 3-D vector:
• 􀀖 = [􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖]􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖
􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖􀀖
• For classification 70% of total dataset is used for
training
• and remaining 30% is used for testing. For k-NN,k=1
was used.

19. EXPERIMENTAL RESULTS
• In this section, the experimental results of k-NN
and SVM
• classifiers are given away.
• Three performance parameters are calculated
using
• following equations:

21. CONCLUSION
• A noninvasive approach has been proposed in
this paper to detect Healthy and DM samples
using color and texture features
• The k-NN and SVM classifiers have been used
to classify DM samples and Healthy samples
from the testing images
• provides classification accuracy up to 94.28%
using k-NN and 97.14% using SVM.

22. reference
• Swapnali N. Padawale, B. D. Jadhav.
"Noninvasive Diabetes Mellitus Detection
Based on Texture and Color Features of Facial
Block" 2016 International Conference on
Automatic Control and Dynamic Optimization
Techniques (ICACDOT) International Institute
of Information Technology (I²IT), Pune IEEE