ABSTRACT
Cervical is the third major disease in developed and developing . Whereas screening and other preventive measures reduce the mortality rate in developed countries, mortality rates still remain very high in developing countries.
This project focuses on the analysis of a digital image of the cervix; captured with a low-level camera, under a contrast agent: the visual with acetic acid (VIA) is known as one of the reference methods to detect cervical cancer.
Gaussian and mean filter techniques were used to remove the speckles. A segmentation algorithm was used to isolate the region of interest (ROI) from the image. Additionally a canny edge detection algorithm was used to find edges.
Furthermore, quantification and classification of the were done. An Android application was used to integrate all the above. This allows usage in rural settings.
The results obtained were quite satisfactory (Specificity 79% and Sensitivity of 83%).
TABLE OF CONTENTS
ABSTRACT……….4
ACKNOWLEDGEMENTS…….5
DEDICATION…………………….6
LIST OF FIGURES…………8
LIST OF TABLES………….9
LIST OF ABBREVIATIONS…….10
CHAPTER ONE INTRODUCTION
1.0 Background of Study….15
1.1Problem Statement….15
1.2Motivation and Purpose…15
1.3Research Contribution………15
1.4Research Objective and Scope…..15
1.5 Target Platform………15
1.6 Thesis Overview………16
1.7 Chapter Summary……16
CHAPTER TWO LITERATURE REVIEW
2.0 Related Works…………17
2.1 Digital Images………………….18
2.1.1 Image Acquisition…….18
2.1.2 Image Pre-processing…….18
2.1.3 Image Segmentation……….19
2.1.4 Image Classifications……..20
2.2 Android Programming……………20
2.2.1 Introduction………….20
2.2.2 The Android Software Stack…..21
2.2.3 Android Building Blocks……..21
2.3 Chapter Summary….24
CHAPTER THREE CERVICAL CANCER
3.0 Introduction……..25
3.1 Anatomy of Human Cervix……………..25
3.2 Development of Precancer and Cancer…….25
3.3 Causes and Symptoms……..25
3.4 Treatment and Diagnosis…………….26
3.5 Colposcopy……….26
3.6 Cervical Cancer Imaging………27
3.7 Chapter Summary…………..28
CHAPTER FOUR APPROACH AND METHODS
4.1 Design Methodology………..29
4.1.1 User Interface Design……29
4.1.2 Image Enhancement…….29
4.1.3 Conversion to greyscale……30
4.1.4 Image Segmentation……….30
4.1.5 Edge detection…………….31
4.1.6 Image Classification…….31
4.1 Conceptual Diagram….31
4.2 System Design……………..31
4.2.1 Activity Diagram………31
4.2.2 Class diagram………………….33
4.3 Chapter Summary…..33
CHAPTER FIVE RESULT AND DISCUSSIONS
5.1 Data Set……….34
5.3 Performance and Measurement……….34
5.3.1 Sensitivity………………..34
5.3.2 Specificity………34
5.4 Results……………..35
5.4 Discussion……….37
5.5 Chapter Summary…….37
CHAPTER SIX SUMMARY, CONCLUSION AND FUTURE WORK
6.1 Summary………………38
6.2 Conclusion…….38
6.3 Future Work……38
REFERENCES…..39
APPENDIX……….41
INTRODUCTION
1.1 Background of Study
Cervical cancer is one of the curable types of cancers in women if detected early. Most cases of cervical cancer are caused as a result of infection with certain types of Human Papillomavirus (HPV) [4, 6].
Although women who have early exposure to sexual relationships and those with multiple sexual partners are at high risk of contracting HPV and eventually, cervical cancer, it is however possible for a woman to be infected with HPV even if she has had only one sexual partner.
In the developed nations, women above the age of 30, who are at high risk of HPV infection, are given HPV vaccines, to reduce the chances of having the disease [4].
Traditionally, optical tests such as VIA, cervicography and colposcopy that employ direct visual examination of the cervix, are becoming popular as a diagnostic tool.
Healthcare professionals study the cervix at about one minute after applying the 5% acetic acid to the cervix area. Acetowhite region (AW), which is the suspected region of cervix, and other vascular abnormalities such as mosaicism, punctuation and vasculature may appear [4].
Cervical cancer is second only to breast cancer as the highest cause of cancer-related death of women in the world [1]. In 2012, it was the fourth leading cause of cancer death in women worldwide with an estimate of about 65,700 deaths.
REFERENCES
American Cancer Society. Cancer Facts & Figures 2015. Atlanta: American Cancer Society; 2015. Available at /www.cancer.org/acs/groups/content/@editorial/…/acspc-044552.pdf
Suleiman Mustafa, Steve Adeshina, Mohammed Dauda and Wole Soboyejo “Classification of cervical cancer tissues using a novel low cost methodology for effective screening in rural settings” IEEE, 2014
World Health Organization Global Health Observatory Data Repository, Mortality and Global Health Estimates 2012
Abhishek Das, Avijit Kar, Debasis Bhattacharyya; “Detection of abnormal regions of precancerous lesions in Digitised Uterine Cervix images”; IEEE, ISBN 978-1-4799-3174- 3/14, 2014
Usman Arshad, Cecilia Mascolo and Marcus Mellor; “Exploting mobile computing in health-care”, IWSAWC, 2014
Abhishek Das, Avijit Kar, Debasis Bhattacharyya, “Elimination of Specular reflection and identification of ROI: The first step in automated detection of cervical cancer using digital colposcopy” IEEE, ISBN 978-1-61284-896-9/11, 2014
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