INTERPRETATION OF PLAIN X-RAY IMAGES USING FUZZY LOGIC TO DETECT AND CLASSIFY

Transcription

1 INTERPRETATION OF PLAIN X-RAY IMAGES USING FUZZY LOGIC TO DETECT AND CLASSIFY BONE TUMORS Yeck Yin Ping Master of Science 2010

2 Pusat Khidmat Maklumat Akademik UNIVERSITI MALAYSIA SARAWAK INTERPRETATION OF PLAIN X-RAY IMAGES USING FUZZY LOGIC TO DETECT AND CLASSIFY BONE TUMORS P. KHIDMAT MAKLUMAT AKADEMIK Iii'I'Vi III II YECK YIN PING A thesis submitted in fulfillment of the requirements for the degree of Master of Science Faculty of Computer Science and Information Technology UNIVERSITI MALAYSIA SARAWAK 2010

3 Acknowledgements First of all, I gratefully acknowledge Unimas Postgraduate Fellowship for financially sponsoring my education and research work. I would like to express my sincere gratitude to my main supervisor, Professor Dr. Wang Yin Chai. His excellent guidance, advice, continuous supports and insightful comments help me throughout all the phase of the research work and thesis write-up. This research has been a very challenging but rewarding experience and I have gained extra knowledge which have made me more confident and self-independent to participate any particular researches in the future. My sincere appreciation is also extended to my co-supervisor, Professor Dr. Pan Kok Long for providing me with the bone tumor x-ray images that are used in this research. He patiently guide me through medical knowledge and generously shared his expertise, assisted me to write several publications and conduct a successful research. Special thanks are dedicated to all postgraduate committee members from Faculty of Computer Science and Information Technology for sharing their valuable suggestion, and great ideas. Furthermore, I would also like to thank my friends in the lab for the fruitful discussions and invaluable help. Finally, I am very grateful to my parents for their spiritual support and love. They are always there for me in any situation to make my life and gained this experience a lot easier. i

4 Abstract C Radiographic is a conventional x-ray image, typically the first imaging test used to diagnose bone tumor. Probably the most common use of x-ray image is to assist the medical experts in detecting the early stages of benign tumor growth, identifying tumor suspicious location and monitoring the progression of degenerative tumor. Reading of x-ray image is usually done by medical experts visually. The diagnosis process requires human expert's cognitive. It depends extremely on the knowledge and long term diagnosis experiences of the medical experts. At the earliest stage of bone tumors, when they are small and difficult to recognize, the radiological finding can lead to potential misidentification and increase the frequency of human error. Meanwhile, different medical experts have different perception of bone tumors because the variable distributions of tumor appeared in x-ray images have presented ambiguity. To help in overcoming such problems, an x-ray interpretation method based on fuzzy logic has been developed in this studyýthis method allows the interpretation of plain x- ray images to be performed semi automatically involving minimum number of input variables in the detection and classification of bone tumor. In order to ensure that all the abnormalities present as benign or malignant are classified properly, an image enhancement method has been developed to refine the input image based on direct manipulation of pixel in the partial domain. The proposed enhancement method employs image filtering technique with combination of image registration to increase the contrast of tumor region. The developed method has been extensively tested and compared against the Mamdani's fuzzy inference methods in term of accuracy using test samples that were obtained from humeral parts with various intensities on the x-ray images. The result showed that a 87.36% of accuracy rate was achieved in bone tumor detection and a 98.38% of sensitivity was achieved in the 11

5 classification of bone tumor. Demonstrations of the experiment results show the feasibility of the proposed method in detecting the distributed abnormalities and classifying any abnormalities present as benign and malignant tumor. iii

6 Abstrak Radiografi merupakan imej x-ray konvensional yang lazimnya digunakan sebagai ujian pengimejan awal untuk mendiagnos tumor tulang. Biasanya, imej x-ray digunakan untuk membantu pakar perubatan dalam mengesan peringkat awal pertumbuhan tumor, mengenal pasti lokasi tumor dan memantau penyebaran tumor yang progresif. Pemeriksaan imej x-ray lazimnya dilakukan oleh pakar perubatan secara visual. Proses ini memerlukan kepakaran pakar perubatan. Pada peringkat awal pertumbuhan tumor tulang, pakar perubatan sukar mengenal pasti tumor tulang yang kecil dan ketaksaan, terutamanya bagi pakar perubatan yang kurang pengalaman. Ini boleh mengakibatkan peningkatan kesilapan yang dilakukan oleh pakar perubatan dalam penterjemahan imej x-ray. Bentuk tumor tulang yang pelbagai juga boleh mengakibatkan pakar-pakar perubatan mempunyai pengertian dan penterjemahan yang berbeza. Bagi mengatasi masalah tersebut, satu kaedah penafsiran x-ray berdasarkan fuzzy logik telah dibangunkan dalam kajian ini. Kaedah ini membolehkan tafsiran imej x-ray dilakukan secara separuh automatik yang melibatkan bilangan pembolehubah yang minimum dalam pengesanan dan pengelasan tumor tulang. Demi memastikan bahawa kesemua ketaknormalan sel dapat diklasifikasikan dengan betul, kaedah pembaikan imej telah dibangunkan untuk membaiki imej berdasarkan manipulasi piksel dalam turnor tulang. Teknik peningkatan yang dicadangkan dengan menggunakan penapisan imej dan pengabungan imej untuk meningkatkan kontras rantau tumor. Kaedah-kaedah yang dibangunkan telah diuji dan dibandingkan dengan kaedah-kaedah Mamdani. Ini adalah untuk menilai kejituan dalam proses pengesan and pengelasan tumor tulang. Pengkajian ini diuji dengan sampel-sampel yang didapati daripada pelbagai bahagian humerus yang mempunyai intensiti yang berlainan. Kadar ketepatan sebanyak 87.36% telah dicapai dalam pengesanan tumor tulang iv

7 dan kadar kejituan sebanyak 98.38% telah dicapai dalam pengelasan tumor tulang. Keputusan eksperimen menunjukkan bahawa kaedah yang dicadangkan mempunyai kebolehan dalam pengesanan ketaknormalan dan pengelasan tumor tulang. V

8 Pusat Khidmat Maklumat Akademik tjniversiti MALAYSIA SARAWAK Table of Contents Acknowledgements 1 Abstract 11 Abstrak iv Table of Contents V1 List of Figures X1 List of Tables xvi List of Abbreviations xvii Chapter 1 Introduction 1.1 Introduction I 1.2 Research Problem Research Objectives Scope Chapters Outline 5 Chapter 2 Literature Review 2.1 Introduction Radiographic Preprocessing and Radiographic Enhancement Past Investigations on the Detection and Classification of Tumors Neural Networks Fractal Theory Template Matching 16 vi

9 2.3.4 Wavelet Approaches Statistical or Texture Analysis Methods Morphological Operations Fuzzy Logic Comparative Studies Summary 35 Chapter 3 Fuzzy Tumor Detection 3.1 Introduction X-ray Images Preprocessing Fuzzy Logic Based Knowledge Formalization Fuzzy Variable Identification and Rejection Mean Intensity Maximum of Gradient Amplitude Euclidean Distance Skewness Fuzzification Fuzzy Variables Normalization Quantization Assignment of Fuzzy Membership Functions Fuzzy Membership Functions for Mean Intensity Fuzzy Membership Functions for Maximum of Gradient Fuzzy Membership Functions for Skewness Fuzzy Inference Rules Setup 59 Vll

10 3.7 Defuzzification Summary 63 Chapter 4 Fuzzy Tumor Classification 4.1 Introduction Partial Tumor Enhancement Image Filtering Mean Filter Median Filter Gaussian Blur Filter Image Registration Fuzzy Logic Based Knowledge Formalization Fuzzy Variable Identification and Rejection Intensity Difference Standard Deviation Fuzzification Fuzzy Variables Normalization Quantization Assignment of Fuzzy Membership Functions Fuzzy Inference Rules Setup Defuzzification Summary 95 viii

11 Chapter 5 Results and Analysis 5.1 Introduction Data Acquisitions X-ray Image Preprocessing Analysis Preprocessing Analysis on High Frequency X-ray Images Preprocessing Analysis on Low Frequency X-ray Images Preprocessing Method Selection Comparison Against Ordinary Images and Enhanced Images Fuzzy Context Selection Radiographic Analysis Intensity Characteristics of Normal Radiographic Intensity Characteristics of Abnormal Radiographic Fuzzy Variables Analysis for Tumor Detection Mean Intensity Analysis on Healthy Samples Mean Intensity Analysis on Abnormal Samples Euclidean Distance Analysis Maximum of Gradient Amplitude Analysis Skewness Analysis Fuzzy Variables Selection for Tumor Detection Fuzzy Membership Interval Analysis for Turpor Detection Fuzzy Defuzzification Method Selection for Tumor Detection Fuzzy Tumor Detection Benchmark Partial Filter Selection Full Filter Analysis 129 ix

12 5.13 Intensity Difference Analysis for Benign and Malignant Tumors Standard Deviation Analysis for Benign and Malignant Tumors Fuzzy Membership Interval Analysis for Tumor Classification Fuzzy Defuzzification Method Selection for Tumor Classification Fuzzy Tumor Classification Benchmark Summary 150 Chapter 6 Conclusions 6.1 Summary Research Contributions Future Works 158 References 160 Appendix I Fuzzy Tumor Detection Benchmark for Noise and Noise-free 168 X-ray Images Appendix II Fuzzy Tumor Classification Benchmark for Noise and Noise- 190 free X-ray Images Appendix III List of Published Papers 203 X

13 List of Figures Figure 2.1 Neural networks structure (Demuth et al., 2008) 11 Figure 3.1 Horizontal projection block diagram 43 Figure 3.2 Block diagram of Euclidean distance measurement on healthy 44 bone image Figure 3.3 Block diagram of Euclidean distance measurement on bone tumor 45 image Figure 3.4 Block diagram of symmetric distribution of skew data sets 46 Figure 3.5 Block diagram of asymmetric distribution of skew data sets 46 Figure 3.6 L-function for linguistic value of A, Lo 51 Figure 3.7 Triangular function for linguistic value of A, Med 52 Figure 3.8 F-function for linguistic value of A, Hi 52 Figure 3.9 The membership functions used in classifying the A, value 53 Figure 3.10 L-function for linguistic value of A,, Lo 54 Figure 3.11 Triangular function for linguistic value of A,,, Med 54 Figure 3.12 F-function for linguistic value of AHi 55 Figure 3.13 The membership functions used in classifying the A,, value 56 Figure 3.14 L-function for linguistic value of A.. Lo - 57 Figure 3.15 Triangular function for linguistic value of A, Med 57 Figure 3.16 F-function for linguistic value of A, Hi 58 Figure 3.17 The membership functions used in classifying the A, value 59 X1

14 Figure 3.18 Cube FAM and sliced cube FAM representations 60 Figure 4.1 The mechanism of image registration 69 Figure 4.2 Subtraction from the image at the left to create the image at right 70 after OR operation is being utilized Figure 4.3 The enhanced image is created after AND operation is being 70 applied Figure 4.4 Block diagram of intensity difference between bone lesion and 75 healthy bone Figure 4.5 Block diagram of selecting and calculating the distributed bone 76 lesion Figure 4.6 Fuzzy classifier control process 77 Figure 4.7 L-function for linguistic value of AdVLo 80 Figure 4.8 Triangular function for linguistic value of AdLo 81 Figure 4.9 Trapezoidal function for linguistic value of AdMed 81 Figure 4.10 Triangular function for linguistic value of AdHi 82 Figure 4.11 F-function for linguistic value of AJVHi 83 Figure 4.12 The membership functions used in classifying the A,, value 83 Figure 4.13 L-function for linguistic value of AVLo 94 Figure 4.14 Triangular function for linguistic value of A, Lo 85 Figure 4.15 Triangular function for linguistic value of A%Med 85 Figure 4.16 Triangular function for linguistic value of A, Hi 86 Figure 4.17 F-function for linguistic value of AVVHi 87 X11

15 Figure 4.18 The membership functions used in classifying the Av value 87 Figure 5.1 Comparison between enhanced images and high frequency image 99 Figure 5.2 Comparison between enhanced images and low frequency image 100 Figure 5.3 Comparison between ordinary images and enhanced images 102 Figure 5.4 Intensity characteristics of healthy sample 105 Figure 5.5 Results of threshold value for healthy samples 106 Figure 5.6 Malignant tumor 107 Figure 5.7 Benign tumor 107 Figure 5.8 Intensity characteristics of abnormal sample 108 Figure 5.9 Results of threshold value for normal and abnormal samples 108 Figure 5.10 Mean intensity histogram of healthy sample Figure 5.11 Mean intensity histogram of healthy sample Figure 5.12 Mean intensity histogram of healthy sample Figure 5.13 Mean intensity histogram of abnormal sample Figure 5.14 Mean intensity histogram of abnormal sample Figure 5.15 Mean intensity histogram of abnormal sample Figure 5.16 The comparison of Euclidean distance between healthy bone and 113 bone lesion Figure 5.17 Maximum of gradient values for normal and abnormal images 113 Figure 5.18 The skewness measurement based on asymmetric and symmetric 114 distribution Figure 5.19 Construction of fuzzy membership interval for mean intensity 117 Figure 5.20 Optimal frequency for linguistic value of A, Lo 118 X111

16 Figure 5.21 Optimal frequency for linguistic value of A, Med 118 Figure 5.22 Optimal frequency for linguistic value of A, Hi 119 Figure 5.23 Construction of fuzzy membership interval for maximum gradient 120 Figure 5.24 Optimal frequency for linguistic value of A. Lo 121 Figure 5.25 Optimal frequency for linguistic value of AmMed 121 Figure 5.26 Optimal frequency for linguistic value of A, Hi 123 Figure 5.27 Construction of fuzzy membership interval for skewness 123 Figure 5.28 Optimal frequency for linguistic value of A,. Lo 124 Figure 5.29 Optimal frequency for linguistic value of A, Med 124 Figure 5.30 Optimal frequency for linguistic value of A, Hi 125 Figure 5.31 Optimal value of mean filter 130 Figure 5.32 Optimal value of median filter 131 Figure 5.33 Optimal value of Gaussian blur filter 132 Figure 5.34 Intensity difference of benign and malignant bone lesion 136 Figure 5.35 Standard deviation of benign and malignant bone lesion 137 Figure 5.36 Construction of fuzzy membership interval for intensity difference 139 Figure 5.37 Optimal frequency for linguistic value of AVLo 140 Figure 5.38 Optimal frequency for linguistic value of AdLo 140 Figure 5.39 Optimal frequency for linguistic value of AdMed 141 Figure 5.40 Optimal frequency for linguistic value of Ad Hi 142 Figure 5.41 Optimal frequency for linguistic value of AdVHi 142 Figure 5.42 Construction of fuzzy membership interval for standard deviation 144 xiv

17 Figure 5.43 Optimal frequency for linguistic value of A, VLo 145 Figure 5.44 Optimal frequency for linguistic value of A, Lo 145 Figure 5.45 Optimal frequency for linguistic value of A, Med 146 Figure 5.46 Optimal frequency for linguistic value of AVHi 146 Figure 5.47 Optimal frequency for linguistic value of AVVHi 147 Figure 5.48 Mean of benign and malignant bone tumor samples 149 xv

18 List of Tables Table 2.1 Comparison among the current studies and clinical applications 34 Table 3.1 Categorization of fuzzy variables into status, ratio range and 50 linguistic value Table 4.1 Categorization of fuzzy variables into status, ratio range and 79 linguistic value Table 4.2 The fuzzy rules table for fuzzy tumor classification 88 Table 5.1 Contrast refinement rates for each preprocessing method 101 Table 5.2 Results of difference between ordinary image and enhanced 103 image Table 5.3 Data overlapped rate for fuzzy variables 115 Table 5.4 Abnormality detection rate for fuzzy defuzzification methods 126 Table 5.5 Benchmarking proposed approach and Pandey approach 127 Table 5.6 Feature enhancement rates for each filter type 128 Table 5.7 Refinement rates from each mask radius of the partial filters 133 Table 5.8 Refinement rates of the partial filters 133 Table 5.9 Benign and malignant refinement rates for each mask radius 134 Table 5.10 Tumor classification rate for fuzzy defuzzification methods 147 Table 5.11 Benchmarking proposed approach and Jain approach 149 Table 5.12 Data overlap rate for fuzzy variable of mean 150 xvi

19 List of Abbreviations ANFIS Adaptive Neuro-Fuzzy Inference System BN Bayesian Networks CAD Computer Aided Diagnosis CPFIS Characteristic-point-based Fuzzy Inference System CT Computed Tomography DroG Derivative of Gaussian FAM Fuzzy Associative Memory fbm fractional Brownian motion FD Fractal Dimension FL Fuzzy Logic FLAIR Fluid-attenuated Inversion-recovery FPCM Fuzzy Possibilities C-Mean FT Fractal Theory GD Gradient Decent GMM Gaussian Mixture Models ICA Independent Component Analysis LSE Least-Squares Estimator MF Membership Functions MO Morphological Operations MRI Magnetic Resonance Imaging NN Neural Network PCA Principal Component Analysis xvii

20 PSNR Peak Signal-to-Noise Ratio PTPSA Piecewise Triangular Prism Surface Area RBF Radial Basis Function ROI Region of Interest ROS Region of Suspicious SGLD Spatial Grey Level Dependency SOM Self-organizing Map STA Statistical or Texture Analysis Methods SVM Support Vector Machine TM Template Matching WA Wavelet Approaches WBC Wisconsin Breast Cancer WDBC Wisconsin Diagnostics Breast Cancer xviii

21 Chapter 1 Introduction 1.1 Introduction Bone cancer is a case where abnormal cells grow rapidly without any order and destroy the healthy tissues. Bone metastasis occurs when the cancer cells lose the ability to control abnormal cells growth and spread to other parts of the body through the bloodstream. The trend can cause great morbidity including debilitating pain and pathologic fractures (Yao et al., 2006). It will threaten human's life dangerously. Primary prevention seems impossible since the causes of this disease still remain unknown as bone cancer is not contagious and inherited through faulty gene. Hence, early detection at the level of expert recognition of a medical specialist is more reliable to improve the bone cancer prognosis. The conventional X-ray image is a useful tool with the highest sensitivity for detecting early bone cancer and their distributions in bones. It is yielding a significant improvement in bone cancer survival. The strength of bone x-ray image lies in its unequalled ability to detect bone cancers in early stages, before tumors have invaded and destroyed nearby healthy tissues and organs. Through its abilities to detect the density of soft tissue contrast, bone x-ray image is capable to show the location, size and shape of bone tumor, as well as monitor the progression of malignant tumor. At the earliest possible stage of bone tumors, when they are small and difficult to recognize, the medical experts manually check for the conventional radiographic features of cartilage to find the distributed abnormalities in bones. The diagnosis requires human expert's cognitive, which is due to the long term experiences and knowledge of medical expert by visually I

22 inspecting the bone x-ray images. The basic studies and clinical applications towards the development of modern computerized scheme are carried out for the detection and characterization of lesions radiographic images. This is not only designed to provide an effective second reader information to the radiologists in the diagnosis of x-ray images, but it also has the capability to diagnose the presence of lesion on medical images of healthy people for bone cancer control. The diagnosis process involves several levels of uncertainty and imprecision and it has been the major challenge in the field of computer vision. The ultimate challenge is the integration of knowledge and experience of medical experts with intelligent computer to produce output of computerized analysis and characterization of medical images for diagnosing purposes. Current studies toward the investigation for detection and classification of bone lesion is based on fuzzy logic technique. Fuzzy logic is applied to deal with the problem of knowledge representation in an uncertain and imprecise interpretation that heavily relies on domain knowledge and expert analysis which therefore rule out the conventional diagnosis methods. In this research, it first uses fuzzy logic for tumor detection and then the detected region is enhanced to extract the desired features for tumor classification. The abnormalities are decoded to tell whether the tumor is benign or malignant. Expert knowledge is utilized to set the fuzzy parameters of membership functions based on individual images, and then come up with rules of data selection and extraction. 2

23 1.2 Research Problem Diagnostics on bone tumors researches are relatively rare. The clinical symptoms are usually unspecific and therefore most tumors are discovered accidentally during routine radiological exams. Reading of bone x-ray image is generally done by medical experts visually, to detect and interpret any abnormalities present, as benign or malignant. This is a labour intensive task as it requires multiple reading of a single x-ray image in order to increase reliability. The different diagnoses of bone tumors in pathology are difficult and can be based on the radiological findings, for example define and diffuse the structure of tumor region in the x-ray images. The abnormalities presented on x-ray images can be extremely small and difficult to recognize. It can lead to potential misidentification for the medical doctors who have less experience and do not have specialization on bone tumor diagnosis. An individual abnormal region may manifest itself differently depending on a benignancy or malignancy of tumor as tumor appears in different sizes, various densities and irregular shapes. The variable distributions of tumors appear in bone x-ray images have presented ambiguity in the field of medical diagnosis. This will cause fuzziness in human perception. Many bone x-ray images exhibit poor contrast with non-uniform background illumination. In particular, the abnormal cell boundaries are not sufficiently sharp to be readily extracted. A major element of this error lies in the failure of the reader to detect signs of abnormality, in which high intensity pixels in the form of white patches scattering in the image can have a number of interpretations. 3

24 1.3 Research Objectives This research is primarily aimed at the investigation of technique for the interpretation of plain x-ray images that serves as a diagnostic aid for medical doctors who have less experience in detecting and classifying the bone tumor. The specific objectives of this research constitute an important achievement of the long term work outlined as follows: " To investigate the potential of fuzzy logic and adopt fuzzy logic to develop an x-ray interpretation method that will be able to detect the bone tumors with various densities and classify the abnormalities present, as benign and malignant tumors. " To develop an image enhancement method that will be able to provide the best separation to distinguish between tumor and its surrounding soft tissue background. " To identify the appropriate and efficient linguistic variables from domain knowledge to deal with reasoning into the use of approximate information and uncertainty to generate decisions in the fuzzy detection and fuzzy classifier. " To validate the proposed method in term of accuracy through the comparison against Mamdani's fuzzy inference methods. 1.4 Scope The overall scope of work reported in this research is focused on techniques for tumor diagnosis operated semi automatically in uncertainty environment. The detection of tumor is done simultaneously with normal and abnormal features recognition using fuzzy logic method. The regions of suspicious with various densities are extracted to differentiate whether the desired location is normal or abnormal class. Then the abnormal region is selected for patial enhancement and feature generator as the knowledge of the extent of tumor is important 4