This document presents a method for analyzing pancreas histological images to identify glucose intolerance using wavelet decomposition. 134 pancreas images from rats were used, with 56 normal and 78 pre-diabetic. Wavelet analysis was used to segment beta cell regions. Features like area ratios were extracted and classifiers like SVM, MLP, Naive Bayes were applied, achieving up to 84% accuracy. Future work could introduce more robust features and faster segmentation methods to improve classification.

1. Analysis of Pancreas Histological Images
for Glucose Intolerance Identification
using Wavelet Decomposition
Presented in
5th International Conference on Frontiers of Intelligent Computing :
Theory and applications (FICTA 2016)
At
KIIT University, Bhubaneswar
Prepared By
Tathagata Bandyopadhyay

2. Overview
 Introduction
 Dataset Description
 Hardware and Software
Configuration
 Different regions of Interest
 Flow chart
 Wavelet Analysis
 Haar wavelet
 Co-occurrence matrix
 Cluster shade matrix
 Colour Enhancement
 ‘Pre-Mask’ and Connected
Component Analysis
 ‘Final Mask’ and Masking Original image
 Image segmentation result
 Intermediate images
 Feature extraction and classification
 Classification Results
 Conclusion and future scope
 Acknowledgement
 References

3. Introduction
 The challenge related to conventional clinical wisdom and expertise level has
paved the way to develop Automatic Computer aided Diagnostic System.
 The broad goal of automatic computerized diagnostic system is to improve
patient care by moving from an experienced based form of clinical practice to
one informed by computer aided systematic application of medical
knowledge.
 In the present interest, it is being observed that diabetic prone rats develop a
cell mediated auto immune destruction of β-cells in pancreas resulting in an
abrupt onset of diabetes Maleates.
 To automate the process of diabetes detection, an approach of automatic
segmentation of images of pancreatic cells of Wistar rats by using wavelet
analysis has been developed in the present paper.
 The process is followed by connected component analysis to automatically
segment the islets of Langerhans region from the histological images of rats'
pancreas cell in order to calculate some morphological features to classify the
images as glucose tolerant (normal) or glucose intolerant (pre-diabetic)[1].

4. Dataset Description
 Experiment is done with a set of 134 pancreas histological images of
Wistar rats.
 Among total 134 images, 56 images are from Normal type rats and rest
78 images are from pre-diabetic type rats.
 For each of the image resolution is 1280 X 960
 Dataset is collected from Department of Biology, University of Évora,
Portugal
Normal Pre-diabetic

5. Hardware and software configuration
 Hardware configuration:
 Intel core i5 1.3 GHz processor
 8GB DDR3 RAM
 500 GB HDD
 Operating system:
 Windows 8, 64 bit
 Software used:
 For image processing and feature extraction:
 MATLAB R2014a
 For classification
 Weka 3.6
 For graphical data representation
 Microsoft Excel 2007

6. Different Regions of Interest (ROI)
 In this article three regions of interest[1] are considered which are as
follows:
 ROI 1 : Islets of Langerhans region including the β-cell region.
 ROI 2 : Only the β-cell region
 ROI 3 : ROI 1 – ROI 2

7. Flow Chart

8. Wavelet Analysis
 It is used to segment out the reddish brown β-cell regions inside the Islets of
Langerhans.
 Starting from the top left corner, single level 2D discrete wavelet transform
(2DDWT) is applied on each consecutive (overlapping) 32 X 32 sub image
blocks of the blue channel of the original RGB image.
 Each sub image blocks are overlapping but differs by one row or column of 1
pixel width in the spatial location from its adjacent sub image block [2].
 For each sub image block, 1 approximation coefficient (cA) and 3 detail
coefficients (cH,cV,cD) of wavelet decomposition are obtained.
 Haar wavelet is used for 2D-DWT.

9. Original
Image
Convert to
Gray scale
BinarizationHole fillingResizeComplement
Blue
Channel
2D
DWT
Cluster
Shade
Matrix
Complemented
Image
A separate flowchart for the wavelet analysis part is shown below:-

10. Co-occurrence Matrix
 For each sub image blocks 4 co-occurrence matrices (1 from the
original sub image block and other 3 from the 3 detail coefficients cH,
cV, cD of DWT) are calculated using 8 gray level approximation.
 Average co-occurrence matrix is formed for each sub image block
using those 4 co-occurrence matrices as mentioned above.
 The average co-occurrence matrix for each sub image block, is
normalized.

11. Cluster Shade Matrix
 For the each sub image block cluster shade[2] value is calculated using the
formula : 𝐶𝑙𝑢𝑠𝑡𝑒𝑟 𝑆ℎ𝑎𝑑𝑒 = σ𝑖,𝑗=1
𝑁
𝑖 − 𝑀 𝑥 + 𝑗 − 𝑀 𝑦
3
𝐶(𝑖, 𝑗)
 Where Mx= σ𝑖,𝑗=1
𝑁
𝑖𝐶(𝑖, 𝑗) and My =σ𝑖,𝑗=1
𝑁
𝑗𝐶(𝑖, 𝑗)
 N = 8 as 8 gray level approximation is used for calculating co-occurrence
matrices.
 Cluster shade values for all the sub image blocks are stored in a 2d matrix
called Cluster Shade Matrix.
 Cluster Shade Matrix is converted to a grayscale image.
 Resulting gray scale image is converted to binary image.

12. Colour Enhancement
 This is done to make the Islets of Langerhans area surrounding the β-cell
region more conspicuous.
 Here following operations are done in order:
 RGB stretching
 HSV stretching
 Sharpening
 Gray scale conversion
 Top hat filtering
 Contrast adjustment
 All the above operations are done using Matlab built-in functions in image
processing tool box.

13. ‘Pre-Mask’ and Connected Component
analysis
 The last image obtained in the colour enhancement portion is masked with
the complement of the resized binary image obtained in the wavelet analysis
portion.
 Resulting image is converted to binary image.
 Now OR operation is done between the binarised image above and the resized
binary image obtained in the wavelet analysis portion.
 Hole filling and morphological opening (with square structuring element of
width 5 pixel) is done. Resulting image is named as ‘Pre-Mask’.
 Keeping only the largest connected component in ‘Pre-Mask’, all other
connected components are discarded to obtain the ‘final mask’ or ‘ROI1’

14. ‘Final-Mask’ and Masking Original image
 Keeping only the largest connected component in ‘Pre-Mask’, all other
connected components are discarded to obtain the ‘final mask’ or ‘ROI1’.
 Original image is masked with the ‘final_mask’ to segment out the β-cell
region along with the surrounding islets of Langerhans region.
 Blue channel is extracted from this masked image and then the contrast
adjustment is done.
 On resulting image adaptive thresholding [5](median method with 32 window
size) is performed to separate out only the β-cell region from the mixture of
β-cell and islets of Langerhans.
 Resulting image is named as 'roi2‘
 Another region, 'roi3' is calculated by subtracting 'roi2' from 'roi1'.

15. Image Segmentation Result
Manual Segmentation using
ImageJ (image courtesy : [1] )
Automatic segmentation using
proposed algorithm

16. Intermediate images

17. Feature Extraction and Classification
 Features used are:
 Ratio of the area of the β-cells to the area of islets of Langerhans
 Perimeter per area ratio of the islets of Langerhans region
 Ratio of the area of ‘ROI2’ to the area of ‘ROI1’.
 Classifiers Used
 OneR (With default Weka setup )
 J48 tree (With default Weka setup)
 SVM (nu-SVC Sigmoid kernel rest are weka default)
 MLP (With default Weka setup)
 Naïve Bayes (With default Weka setup)

18. Classification Results (tabular)
Classifier
Correctly
Classified
(in percent %)
TP rate FP rate Precession recall F-measure ROC area
OneR 70.8955 0.709 0.305 0.71 0.709 0.709 0.702
J48 tree 78.3582 0.784 0.211 0.79 0.784 0.785 0.827
SVM 82.0896 0.821 0.169 0.829 0.821 0.822 0.826
MLP 84.3284 0.843 0.133 0.861 0.843 0.844 0.866
Naïve
Bayes
71.6418 0.716 0.319 0.714 0.716 0.712 0.837

19. Classification Results (graphical)
60
65
70
75
80
85
90
Accuracy
Classifier performance -
Accuracy
OneR
J48 Tree
SVM
MLP
Naive Bayes
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
AreaUnderROCcurve
Classifier performance -
ROC Area
OneR
J48 Tree
SVM
MLP
Naive Bayes

20. Conclusion and Future scope
 The proposed segmentation model is capable enough to automatically segment out the β-cell
regions along with the surrounding Islets of Langerhans.
 Classification results are satisfactory as majority of the classifier gives accuracy above 75% and
ROC area more than 0.8.
 However, accuracy can vary depending on the classifiers and their specification selected.
 Future Scope:
 Introduction of more robust features to improve classification accuracy
 Instead of wavelet analysis some other techniques can be used to speed up the segmentation process.
 Some algorithmic modification is required for more effective processing of the images with multiple β-
cell regions.

21. Acknowledgement
The authors thank Professor Fernando Capela e Silva, from
the Department of Biology and Ana R. Costa and Célia M.
Antunes, from the Department of Chemistry, University of
Évora, Portugal, for the data set used in this article.

22. References
 [1] L. M. Rato, F. C. e Silva, A. R. Costa, and C. M. Antunes, “Analysis of
pancreas histological images for glucose intolerance identification using
imagej-preliminary results,” in 4th Eccomas Thematic Conference on
Computational Vision and Medical Image Processing (VipIMAGE), 2013, pp.
319–322.
 [2] S. Arivazhagan and L. Ganesan, “Texture segmentation using wavelet
transform,” Pattern Recognition Letters, vol. 24, no. 16, pp. 3197–3203,
2003.
 [3] A. Gavlasov´a, A. and Proch´azka, and M. Mudrov, “Wavelet based
image segmentation,” in Proceedings of the 14th Annual Conference
Techincal Computing, 2006, pp. 1–7.
 [4] “http://in.mathworks.com”, (last accessed 15th June 2016) .
 [5] “http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm.”, (last
accessed 15th June 2016) .
 [6] B. Nunes, L. Rato, F. Silva, A. Rafael, and A. Cabrita, “ Processing and
classification of biological images,” in Technology and Medical Sciences, CRC
Press, 2011, pp. 233 –237.

23. Thank You