CALIFORNIA STATE UNIVERSITY, NORTHRIDGE. Diagnosis of Tuberculosis using Fuzzy Logic & Image Processing

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1 CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Diagnosis of Tuberculosis using Fuzzy Logic & Image Processing A graduate project submitted in partial fulfillment of the requirements For the degree of Master of Science in Electrical Engineering By Sunnykumar Hirpara May 2017

2 The graduate project of Sunnykumar A. Hirpara is approved: Dr. Xiaojun (Ashley) Geng Date Dr. Ali Amini Date Dr. Ruting Jia, Chair Date California State University, Northridge ii

3 Acknowledgement It is my privilege to express my sincerest regards to my project mentor Dr. Ruting Jia for her exceptional support and guidance in the field of Fuzzy Logic throughout my graduate project. During my work, she helped with each and every problem with utmost honesty and enthusiasm. Also Dr. Jia provided me with many opportunities including writing a research paper together for this topic and a mentorship program for a renowned company. Furthermore, I would like to thank my committee members Dr. Xiaojun (Ashley) Geng and Dr. Ali Amini for their interest and constant support in my work. iii

4 Dedication To my grandfather who is no more in this world but still in my heart for constantly inspiring me to work harder. My family, which is my backbone and energy that keeps me motivated and optimistic. Furthermore, my friends that brings the best version of me and pushing me forward to do my best. iv

5 Table of Contents Signature page... ii Acknowledgement... iii Dedication... iv List of Tables...vii List of Figures...viii Abstract...x 1. INTRODUCTION Fuzzy Logic Image Processing DIAGNOSIS OF TUBERCULOSIS USING FUZZY LOGIC & IMAGE PROCESSING Overview Linguistic Variables Input Variables 4 1) Coughing more than 3 weeks..4 2) Coughing Blood..5 3) X-ray test.6 4) Chest Pain 7 5) Tuberculin skin test.8 6) Night Sweats...9 7) Unintentional Weight loss.10 8) Smoking Output Variable. 12 v

6 3. FUZZY RULES Fuzzy if-then rules for Diagnosis of TB RESULTS I (USING FUZZY LOGIC) GRAPHICAL USER INTERFACE RESULTS II (USING NETBEANS IDE 8.2) IMAGE PROCESSING Methods RESULTS III (USING IMAGE PROCESSING) Image Enhancement 23 A. Gabor Filter Image Segmentation 24 A. Thresholding Approach..24 B. Marker-Controlled Watershed Segmentation Approach Features Extraction 25 A. Binarization CONCLUSION 26 REFERENCES. 27 APPENDIX A.29 APPENDIX B.30 vi

7 List of Tables Table 2.1: Membership functions for Coughing more than 3 weeks Table 2.2: Membership functions for Coughing Blood Table 2.3: Membership functions for X-ray test Table 2.4: Membership functions for Chest Pain 7 Table 2.5: Membership functions for Tuberculin skin test.. 8 Table 2.6: Membership functions for Night Sweats 9 Table 2.7: Membership functions for Unintentional Weight Loss Table 2.8: Membership functions for Smoking Table 2.9: Membership functions for Diagnosis vii

8 List of Figures Figure 1.1 Structure of a Fuzzy Controller.1 Figure 1.2 TB image Processing stages..2 Figure 2.1 Block diagram of Fuzzy Controller....3 Figure 2.2 Membership function plot for coughing more than 3 weeks Figure 2.3 Membership function plot for Coughing Blood.5 Figure 2.4 Membership function plot for X-ray test...6 Figure 2.5 Membership function plot for Chest Pain..7 Figure 2.6 Membership function plot for Tuberculin skin test. 8 Figure 2.7 Membership function plot for Night Sweats..9 Figure 2.8 Membership function plot for Unintentional Weight Loss Figure 2.9 Membership function plot for Smoking Figure 2.10 Membership function plot for Diagnosis Figure 3.1 Fuzzy Logic if-then rules example with graph..13 Figure 3.2 Rule Editor for the Fuzzy Controller.15 Figure 4.1 Rule viewer for Rule Figure 4.2 Rule viewer for Rule Figure 4.3 Rule viewer for Rule Figure 4.4 Rule viewer for Rule Figure D Surface viewer for the Fuzzy Controller 18 Figure 5.1 GUI created in NetBeans IDE Figure 6.1 GUI output for Rule viii

9 Figure 6.2 GUI output for Rule Figure 6.3 GUI output for Rule Figure 6.4 GUI output for Rule Figure 7.1 Block Diagram for TB X-rays Image Processing 22 Figure 8.1 Image Enhancement using Gabor Filter 23 Figure 8.2 Image segmentation of a Gabor Enhanced image using Thresholding 24 Figure 8.3 Image segmentation of a Gabor Enhanced image using Watershed approach.24 Figure 8.4 Binarization method procedure 25 Figure 8.5 Normality and Abnormality of images by Binarization Procedure 25 ix

10 Abstract Diagnosis of Tuberculosis using Fuzzy Logic & Image Processing By Sunnykumar Hirpara Master of Science in Electrical Engineering The project consists of a diagnosis system based on fuzzy logic to determine the level of sickness of a TB patient and also to determine the disease with symptoms as inputs. The healthiness of the person s body is measured in the range of 0 to 10. The diagnostics system is implemented in the fuzzy logic toolbox built in the MATLAB software. We have to design a GUI in NetBeans IDE for more user friendly experience and allowing the users to determine their healthiness in terms of tuberculosis diagnosis. Also, using MATLAB we will take X-ray images of lungs with inflammation and determine the level of severity of the disease using image processing tool. The inflammation will determine whether the patient is having mild or serious condition. x

11 1. INTRODUCTION 1.1 Fuzzy Logic Professor Lotfi A. Zadeh introduced the concept of Fuzzy logic in 1965 at Univeristy of California, Berkeley. [1] [2] [3] Fuzzy Logic initiates with the building of a data of human made rules and the Fuzzy Inference Systems converts them to the mathematical algorithms or its equivalents. [4] We have models of Fuzzy Logic also known as Fuzzy Inference Systems, have fuzzy if-then rules. [5] There is an important theory of linguistic variables in fuzzy logic which describes the condition of the problem statement as input or output. Fuzzy rules i.e. if-then describes the relationship between two linguistic variables. [6] Fig 1.1 Structure of a Fuzzy Inference System [6] 1

12 1.2 Image Processing Image processing has been used widely in many biomedical applications such as detection of lung cancer, TB diagnosis and CT scan. There are five types of images which are represented in MATLAB as Grayscale, RGB, Indexed, Binary and uint8. Image enhancement is done using Gabor filter or FFT technique. In the process of Image segmentation, the image is divided into its specific regions. To separate different parts of images, features extraction is used which uses various techniques and algorithms. There are two methods for the features extraction: i) Masking ii) Binarization Fig 1.2 TB image processing stages [7] 2

13 2. DIAGNOSIS OF TUBERCULOSIS USING FUZZY LOGIC & IMAGE PROCESSING 2.1 Overview Tuberculosis or commonly known as TB is a highly contagious and life threatening diseases caused by a bacteria called Mycobacterium. It is usually detected in lungs and can spread to many parts such as spine, brain etc. It is spread through air when a person with TB sneezes, coughs or talks. There are two types of TB: i) Latent TB ii) Active TB This project focuses on diagnosis of Tuberculosis using the symptoms provided as input linguistic variables in the fuzzy controller. We have 8 input variables as symptoms and 4 output variables. For example, a person wants to know if he/she is suffering from TB or is having initial symptoms of TB, then using fuzzy logic control, it is possible to determine the level of healthiness or severity of a person. We have defined the four output variables which are divided in the scale of 0 to 10 i.e. from Healthy to Active TB stage. Fig 2.1 Block diagram of Fuzzy Inference System 3

14 2.2 Linguistic Variables They describe the crisp input information in a proper form with accuracy suitable for the given problem Input Variables 1) Coughing more than 3 weeks This variable defines that the person has been coughing for three weeks and it has two membership functions ranging from 0 to 2. MEMBERSHIP FUNCTIONS RANGE NO YES 1 2 Table 2.1 Membership functions for coughing more than 3 weeks Fig 2.2 Membership Function plot for coughing more than 3 weeks 4

15 2) Coughing Blood This variable describes the condition of blood along with the cough. It could be in excess amount or in less amount. So based on this condition, we have 4 membership functions for coughing blood and it ranges from 0 to 20. Rare and Mild are triangular MFs whereas No and Extreme are trapezoidal MFs. MEMBERSHIP FUNCTIONS RANGE NO -7-4 RARE 2-8 MILD 6 12 EXTREME Table 2.2 Membership functions for Coughing blood Fig 2.3 Membership Function plot for Coughing blood 5

16 3) X-ray test X-ray test is done to determine the inflammation in the chest due to the Mycobacterium. It is very important to have the X-ray test as it shows the major problem and patient can start the treatment. It has 3 membership functions out of which, two of them are trapezoidal while one of them is triangular MF. It ranges from MEMBERSHIP FUNCTIONS RANGE NO INFLAMMATION -7-4 MILD INFLAMMATION 2-10 HIGH INFLAMMATION 8-23 Table 2.3 Membership functions for X-ray test Fig 2.4 Membership Function plot for X-ray test 6

17 4) Chest Pain Chest pain usually occurs when a person suffers from TB. It can be rare or regular, so we have 3 MFs for chest pain i.e. No pain, Mild pain and High pain. It ranges from 0 to 10. Two of them are trapezoidal MFs while Rare pain is a triangular MF. MEMBERSHIP FUNCTIONS RANGE NO PAIN -4-3 RARE PAIN 2-7 HIGH PAIN 6-12 Table 2.4 Membership functions for Chest pain Fig 2.5 Membership Function plot for Chest Pain 7

18 5) Tuberculin skin test This test is done by injecting small amount of protein(tb) on your hand. If the red bump appears then you re suffering from TB but this test is not 100% accurate. So we have 2 MFs for this test i.e. No red bump and Red bump. It ranges from 0 to 2 and both of them are trapezoidal MFs. MEMBERSHIP FUNCTIONS RANGE NO RED BUMP RED BUMP Table 2.5 Membership Functions for Tuberculin skin test Fig 2.6 Membership Function plot for Tuberculin Skin test 8

19 6) Night Sweats Sweating at night and shortness of breath usually occurs but it is not necessary factor. Sometimes the night sweats are rare, mild or extreme. So, we have 4 MFs for night sweats i.e. None, Rare, Mild and Extreme. None and Extreme are trapezoidal MFs whereas Rare and Mild are triangular MFs. It ranges from 0 to 20. MEMBERSHIP FUNCTIONS RANGE NONE -7-4 RARE 2-8 MILD 6-12 EXTREME Table 2.6 Membership functions for Night sweats Fig 2.7 Membership Function plot for Night Sweats 9

20 7) Unintentional weight loss Sometimes there is sudden weight loss in TB. It can be Low or High or sometimes there isn t any weight loss. There are 3 MFs and two of them are trapezoidal MFs and one is triangular MF. It ranges from 0 to 20. MEMBERSHIP FUNCTIONS RANGE NO -7-4 LOW 2-10 HIGH 8-23 Table 2.7 Membership functions for Unintentional weight loss Fig 2.8 Membership Function plot for Unintentional Weight Loss 10

21 8) Smoking Studies shows that even smoking is a risk factor for infection of TB virus in lungs. However, the results are not fully accurate. So, we have 4 MFs for smoking. Never and High are trapezoidal MFs while Rarely and Regular are triangular MFs. It ranges from 0 to 20. MEMBERSHIP FUNCTIONS RANGE NEVER -7-4 RARELY 2-8 REGULAR 6-12 HIGH Table 2.8 Membership functions for Smoking Fig 2.9 Membership Function plot for Smoking 11

22 2.2.2 Output variables The output is the resulting diagnostic results by which the person can know the severity of his/her health condition. 1) Diagnosis This is the output variable for the diagnosis of TB and it has 4 MFs i.e. Healthy, Latent TB, Very sick and Active TB. It ranges from 0 to 10. Two of them are trapezoidal MFs and other two are triangular MFs. MEMBERSHIP FUNCTIONS RANGE HEALTHY -3-2 LATENT TB 1-5 VERY SICK 4-8 ACTIVE TB Table 2.9 Membership functions for Diagnosis Fig 2.10 Membership Function plot for Diagnosis 12

23 3. FUZZY RULES We tend to make decisions based on certain conditions or rules. For e.g. If the weather is not rainy and the roads are not slippery then we will go for a dinner date. In this sentence, the person has made two conditions with one outcome. Fuzzy rules are based on this above example. Similarly, fuzzy controllers tend to imitate the actions/behavior of human beings. They are the rules in which their consequences and antecedents are fuzzy and not crisp. Fuzzy rules are the most important part in the mechanism of thinking with multiple conditions. There are many fuzzy sets used in the fuzzy logic applications. Some of them are: i) Fuzzy Conjunction(AND) ii) Fuzzy Disjunction(OR) iii) Fuzzy Complement(NOT) We have used Fuzzy Conjunction (AND) in this project and we have 10 rules for the above 8 input and 1 output variable. Fig 3.1 Fuzzy Logic if-then rules example with graph 13

24 3.1 Fuzzy if-then Rules for Diagnosis of TB 1) If Coughing more than 3 weeks is Yes and Coughing blood is Extreme and X-ray test is High inflammation and Chest pain is High pain and Tuberculin skin test is Red bump and Night sweats is Extreme and Unintentional weight loss is High and Smoking is High, then Diagnosis is Active TB. 2) If Coughing more than 3 weeks is No and Coughing blood is No and X-ray test is No inflammation and Chest pain is No pain and Tuberculin skin test is No Red bump and Night sweats is None and Unintentional weight loss is No and Smoking is Never, then Diagnosis is Healthy. 3) If Coughing more than 3 weeks is Yes and Coughing blood is Rare and X-ray test is Mild inflammation and Chest pain is Rare pain and Tuberculin skin test is Red bump and Night sweats is Rare and Unintentional weight loss is High and Smoking is Regular, then Diagnosis is Latent TB. 4) If Coughing more than 3 weeks is No and Coughing blood is Rare and X-ray test is Mild inflammation and Chest pain is No pain and Tuberculin skin test is No Red bump and Night sweats is Rare and Unintentional weight loss is Low and Smoking is Rarely, then Diagnosis is Latent TB. 5) If Coughing more than 3 weeks is Yes and Coughing blood is Mild and X-ray test is Mild inflammation and Chest pain is Rare pain and Tuberculin skin test is Red bump and Night sweats is Mild and Unintentional weight loss is Mild and Smoking is High, then Diagnosis is Very sick. 6) If Coughing more than 3 weeks is No and Coughing blood is No and X-ray test is Mild inflammation and Chest pain is Rare pain and Tuberculin skin test is No Red bump and Night sweats is None and Unintentional weight loss is No and Smoking is Never, then Diagnosis is Healthy. 7) If Coughing more than 3 weeks is Yes and Coughing blood is Mild and X-ray test is High inflammation and Chest pain is High pain and Tuberculin skin test is Red bump and Night sweats is Extreme and Unintentional weight loss is High and Smoking is Regular, then Diagnosis is Very sick. 14

25 8) If Coughing more than 3 weeks is Yes and Coughing blood is Mild and X-ray test is High inflammation and Chest pain is High pain and Tuberculin skin test is Red bump and Night sweats is Extreme and Unintentional weight loss is High and Smoking is Regular, then Diagnosis is Active TB. 9) If Coughing more than 3 weeks is No and Coughing blood is Rare and X-ray test is No inflammation and Chest pain is No pain and Tuberculin skin test is Red bump and Night sweats is Rare and Unintentional weight loss is Low and Smoking is Regular, then Diagnosis is Latent TB. 10) If Coughing more than 3 weeks is Yes and Coughing blood is Mild and X-ray test is High inflammation and Chest pain is Rare pain and Tuberculin skin test is No Red bump and Night sweats is Extreme and Unintentional weight loss is High and Smoking is Regular, then Diagnosis is Very sick. Fig 3.2 Rule Editor for the Fuzzy Controller 15

26 4. RESULTS - I (USING FUZZY LOGIC) Fig 4.1 Rule viewer for Rule 1 Fig 4.2 Rule viewer for Rule 2 16

27 Fig 4.3 Rule viewer for Rule 3 Fig 4.4 Rule viewer for Rule 5 17

28 Fig D Surface viewer for the Fuzzy Controller From the above simulation results, we can observe that the rules that we have defined can be shown as crisp outputs in the rule viewer window. If we compare the final diagnosis value with the values of the MFs in the Fuzzy controller, the results are accurate. The method that we have used is Centroid with Defuzzification using Mamdani Max-Min. 18

29 5. GRAPHICAL USER INTERFACE For easiness and simulation, a graphic user interface was developed. The GUI was developed with NetBeans IDE 8.2 where IDE stands for Integrated Development Environment which can be used by third party developers for the application development. Fig 5.1 GUI created in NetBeans IDE 8.2 We have added radio buttons as a single input to each of the column and a button on which user will click to get the diagnosis results inside the text field. 1) Label The letters in the blue and red color are labels. A Label is just a text insertion field in which user cannot edit the text or image while running the application. It can display both image and text. 2) Radio Button The options with small white circles are radio buttons. A radio button is a type of option that user can select amongst many. Only one button can be selected at a time. 3) Text Area The white area that can be seen at the bottom is the Text area. A text area is a text box which can either be used as input or output for displaying text. 19

30 6. RESULTS II (USING NETBEANS IDE 8.2) Fig 6.1 GUI output for Rule 1 Fig 6.2 GUI output for Rule 7 20

31 Fig 6.3 GUI output for Rule 4 Fig 6.4 GUI output for Rule 6 21

32 7. IMAGE PROCESSING Tuberculosis is a life threatening disease and about on third population of the world is diagnosed with Tuberculosis. Tuberculosis bacteria Mycobacterium spreads and affects the lungs initially. Chest x-rays are useful for determining the inflammation in the lungs. We have used MATLAB image processing tool for this project. The main aim of the project is to process the X-ray images of lungs and to identify the features with help of Gabor filter and watershed filter followed by masking and pixelization. 7.1 Methods We have 3 stages in the image processing of X-rays which are listed as follows: 1) Image Enhancement: We have used Gabor filter for enhancing the image. This is used to remove the noise and process the image more accurate. Also it is used for the detection of edge in the image. [7] 2) Image Segmentation: This method is used to divide the image and enhance it into different parts. In this method we have used RGB to gray and Watershed marker controlled segmentation which is also known as thresholding. [7] 3) Features Extraction: This method extracts the general features of the image using Binarization and Pixelization approach. [7] Fig 7.1 Block diagram of TB X-rays image processing 22

33 8. RESULTS III (USING IMAGE PROCESSING) 8.1 Image Enhancement A. Gabor Filter It is a type of filter which is linear and its impulse response is given by a function that is multiplied with a function (Gaussian). The figures shown below are a) Original image and b) image enhanced by Gabor Filter. a) Original image b) enhancement by Gabor Filter Fig 8.1 Image Enhancement using Gabor Filter 8.2 Image Segmentation This is the most important process in medical 2D and 3D image processing. This process is used for segmenting different parts in images for medical applications. This process is usually used to point the objects in the images, also boundaries. The main aim of segmentation is to convert or to simplify an image into more understandable way for the user to view. There are various algorithms for segmentation based its two simple properties for values of its intensity: similarity and discontinuity [7]. So we have used Thresholding approach and Marker Controlled Watershed Segmentation approach. 23

34 A. Thresholding approach One of the advantage of thresholding is smaller image space and rapid image processing. The operation of thresholding is non-linear and it converts gray scale image into binary image [7]. a) Enhanced by Gabor Filter b) Image Segmentation by Thresholding Fig 8.2 Image segmentation of a Gabor Enhanced image using Thresholding B. Marker-Controlled Watershed Segmentation Approach This approach indicates the objects which are located in the background at defined locations of images [7]. a) Enhanced by Gabor Filter b) Image Segmentation by Watershed Fig 8.3 Image segmentation of a Gabor Enhanced image using Watershed approach 24

35 8.3 Features Extraction This is a crucial stage for extracting features and various shapes from a given image. To detect the probability of inflammation due to Mycobacterium resulting in TB, Binarization approach is used [7]. A. Binarization In this approach, the number of white pixels is greater than white pixels in the normal healthy lung images. If the number of white pixels is higher than the black pixels, then it states that the lungs are normal and healthy. The flow chart shown below shows check method for the binarization procedure [7]. Fig 8.4 Binarization method procedure [7] a) Normal image (Thresholding) b) Abnormal image Thresholding Fig 8.5 Normality and Abnormality of images by Binarization procedure 25

36 9. CONCLUSION In the process of designing fuzzy controller for the diagnosis of Tuberculosis, we have used 8 input variables and one output variable in MATLAB fuzzy logic designer. The outputs we got in the fuzzy rule viewer are pretty accurate. We have defined each and every membership function within a specific range for every input and output variable. We have concluded that fuzzy control is an appropriate option for the diagnosis of TB. NetBeans IDE 8.2 was used to create a Graphical User Interface (GUI) and to ease the operation. Also, we have used image processing toolbox in MATLAB for the image processing of Lung X-rays. We have used three stages for the image processing including different filters and approaches on 10 subjects with the first five on the training purpose with the Fuzzy inference system and other 5 testing with the image processing tool to avoid overfeeding of data. Concluding the project, the methods used are appropriate and future modifications can be made through various features. 26

37 REFERENCES [1] L.A. Zadeh, Fuzzy Sets, Information and Control, Vol 8, , 1965 [2] L.A. Zadeh, Outline of A New Approach to the Analysis of Complex Systems and Decision Processes, 1973 [3] L.A. Zadeh, "Fuzzy algorithms," Info. & Ctl., Vol. 12, 1968, pp [4] L.A. Zadeh, "Making computers think like people," IEEE. Spectrum, Vol 8, 1984, pp [5] M. Hellmann, "Fuzzy Logic Introduction", March 2001 [6] Mehmet Karakose & Erhan Akin, "BLOCK BASED FUZZY CONTROLLERS", IJRRAS Vol 3, Issue 1, April 2010 [7] Mokhled S. AL-TARAWNEH "Lung Cancer Detection Using Image Processing Techniques", Leonardo Electronic Journal of Practices and Technologies, ISSN , Issue 20, January-June 2012, pp [8] Venkatesh, C., Shaik, F., Imran, G., M., Haneesh, T., "Fuzzy-Neuro Logic In Segmentation Of Mri Images", IEEE-International Conference On Advances In Engineering, Science And Management, pp , March [9] Apopei, V., Bejinariu, S., Costin, H., N., Jitca, D., Luca, M., Nita, c., D., "A Review of Several Applications of FL in Modeling and in ld/2d Signal Processing", International Symposium on Signals, Circuits and Systems (lsscs), pp. 1-4, July [10] Sathish Kumar, S., Moorthi, M., Madhu, M., Amutha, R., "An Improved Method of Segmentation Using Fuzzy-Neuro Logic", Second International Conference on Computer Research and Development, pp , May [11] Wai Yan Nyein Naing, Zaw Z. Htike, "Advances in automatic tuberculosis detection in chest x-ray images", signal & image processing an international journal (sipij) vol.5, no.6, December [12] Joshua M. Leibstein and Andre L. Nel, "Detecting tuberculosis in chest radiographs using image processing techniques", School of Electrical and Electronic Engineering University of Johannesburg 27

38 [13] Stefan Jaeger, Alexandros Karargyris, Serna Candemir, Les Folio, Jenifer Siegelman, Fiona Caliaghan,Zhiyun Xue, Kannappan Palaniappan, Rahul K. Singh, Sameer Antani, George Thoma, YiXiang Wang,Pu-Xuan Lu, and Clement J. McDonald, "Automatic Tuberculosis Screening Using Chest Radiographs", IEEE transactions on medical imaging, vol. 33, no. 2, February [14] Poomimadevi.CS, Helen Sulochana.C, "AUTOMATIC DETECTION OF PULMONARY TUBERCULOSIS USING IMAGE PROCESSING TECHNIQUES", March 2016, pp [15] Gonzalez RC, Woods RE, 1992, Digital image processing, Addison-Wesley 28

39 APPENDIX A Code for NetBeans IDE

40 Code for MATLAB image processing APPENDIX B 30