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Mri-Based Images Segmentation for Gpu Accelerated Fuzzy

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Mri-Based Images Segmentation for Gpu Accelerated Fuzzy MRI-BASED IMAGES SEGMENTATION FOR GPU ACCELERATED FUZZY METHODS ON GRAPHICS PROCESSING UNITS BY CUDA A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Wei-Hung Cheng December 2018 Dissertation written by Wei-Hung Cheng B.B.A., Baylor University, USA 1998 M.S., Texas A&M University-Commerce, USA, 2002 M.S., Texas A&M University-Commerce, USA, 2003 Ph.D., Kent State University, USA 2018 Approved by Dr. Cheng Chang Lu , Chair, Doctoral Dissertation Committee Dr. Austin Melton, Jr. , Members, Doctoral Dissertation Committee Dr. Angela Guercio , Dr. Jun Li , Dr. Robert J. Clements , Accepted by Dr. Javed I. Khan, , Chair, Department of Computer Science Dr. James L. Blank , Dean, College of Arts and Sciences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¶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igure 4.1: Floating-Point Operations per Second for the CPU and GPU. Data of NVIDIA. .................................................................................................................... 28 Figure 4.2: Memory Bandwidth for the CPU and GPU. Data of NVIDIA. ..................... 29 Figure 4.3: Diagram of Block and Thread Organization in CUDA [41] .......................... 32 Figure 4.4: Diagram of Memory Hierarchy in CUDA [41] .............................................. 32 Figure 5.1: 3D image model for DICOM files ................................................................. 34 Figure 5.2: Loading data from DICOM Flowchart ........................................................... 35 Figure 5.3: Cube shape cases ............................................................................................ 35 Figure 5.4: Grid size effect on 3D model resolution ........................................................ 35 Figure 6.1: Proof of segmentation accuracy on the side brain image ............................... 56 Figure 6.2: CPU and GPU Execution Time of FCM-based Algorithms on the Side Brain Image ......................................................................................................................... 57 Figure 6.3: Accuracy of the IT2FCM algorithm ............................................................... 57 Figure 6.4: CPU and GPU Execution Time of FCM-based Algorithms on the Top Brain Image ......................................................................................................................... 58 Figure 6.5: Execution Times of IT2FCM on CPU and GPU ............................................ 59 vi LIST OF TABLES Table 6.1: GPU utilization for different number of threads in % [54] ............................. 51 Table 6.2: Access times of different memory types [54] .................................................. 51 Table 6.3: Configuration of CPU and GPU Hardware ..................................................... 53 Table 6.4: Speed-up for GPU Implementations ................................................................ 56 Table 6.5: The performance of FCM-based algorithm comparison on the side brain image ................................................................................................................................... 56 Table 6.6: The performance of FCM-based algorithm comparison on the top brain image ................................................................................................................................... 57 vii ACKNOWLEDGEMENTS I would like to thank my advisor, Professor Dr. Cheng-Chang Lu, for his continuous guidance, support, and encouragement throughout my Ph.D. study. He has provided me not only a collaborative environment to do research, but also the technical knowledge and a rigorous attitude towards that research. I would like to give sincere thanks to Professor Dr. Melton Jr. Austin, Professor Dr. Angela Guercio, Professor Dr. Jun Li, and Professor Dr. Robert J. Clements for participating in the committee. Thanks for their constructive suggestions and comments on my research and thesis. Meanwhile, I want to thank the wonderful members of our research group at the image processing and computer vision lab. They are Yujun Guo, Chi-Hsiang Lo, Fan Chen, Yufan Liu, and Xinyu Chang. Because of them, the laboratory life became more pleasant and enjoyable. Special thanks for their helpful discussions and friendship. Most of all I am grateful to my family. I thank my sister and my brother for their love and support. I would like to express my deepest thanks to my mother, for her unwavering support and encouragement. Without their support, this thesis could not be done. I dedicate this thesis to them. Wei-Hung Cheng November 16, 2018, Kent, Ohio viii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physician’s attention to any abnormalities in the body tissues such as tumors [3]. Moreover, efficient and effective techniques are required in order to identify the important parts in medical images such as the Regions of Interest (ROI), which helps medical experts to identify the disease. This is achieved via a segmentation operation [4]. In many forms of Computer Aid Diagnosis (CAD), it is based on separating the ROI in order to study it independently [5]. Clustering, region growing and many other methods can be applied to improve or enchance the result of the segementation process. The system for extracting the ROI can be built on two different concepts such as supervised machine learning and unsupervised machine learning. In supervised machine learning, the machine is provided with a set of solution instances of the problem at hand, which may for example be classification or regression. Both are supervised learning methods; they differ in whether the set of potential solutions for each instance is discrete or continuous. On the other side, in unsupervised machine learning, incorrect solutions are provided. In many of cases, the researcher prefers to use unsupervised learning methods over the supervised ones because the supervised methods need time for training. The unsupervised methods segment the ROI directly from the image without inspecting other images [6]. In this research, we are interested in unsupervised segmentation algorithms. We are focusing on the Fuzzy C-means (FCM) clustering algorithm which is one of the very popular 2D image segmentation algorithms for ROI extraction. Moreover, applying the Fuzzy C-mean clustering algorithm for segementing 3D medical volumes has a main problem of efficiency because of its expensive computation time. In many medical 2 applications, the source of the 3D dataset is the acquisition system such as PET, CT, or MRI. A stack of slices is produced, each of which is a 2D medical image that covers a specific section from the human body scan. All slices are grouped together using MATLAB code to form a 3D matrix which evaluates the 3D medical volume [7]. 3D volumes can be evaluated either by the volume they occupy or by their contouring edges. This research will develop an algorithm
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