LEE- Elektronik Mühendisliği-Doktora
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ÖgeEmbedded vision system designed on a heterogeneous computing platform and applied to semen analysis(Lisansüstü Eğitim Enstitüsü, 2021) Şavkay, Osman Levent ; Yalçın, Müştak Erhan ; 709922 ; Elektronik ve Haberleşme MühendisliğiImage and video inspections in the medical field require intelligent systems in addition to microscopes for more accurate and detailed analyses. In this study, a reliable, reconfigurable, and compact intelligent analyzer system is proposed using a hybrid platform comprising a field programmable gate array (FPGA) and central processing unit (CPU) as the embedded video processing system. Computer assisted semen analysis (CASA) is then developed as an application on the proposed system. The developed system architecture can also be utilized in biological imaging and video applications, which require accurate and detailed analysis. Relevant semen analysis algorithms were constructed specially for the proposed architecture; however, the algorithms may also be used with similar existing embedded system platforms and newly developed hardware. As the speeds and performances of CPUs have approached their technological limits, a newer trend has evolved toward parallel computing; more recently, the use of heterogeneous computing platforms have also increased as they have attracted the interest of scientists and system developers owing to their operating performances. They surpass conventional general-purpose CPU-based architectures in terms of capacity, speed, and performance characteristics. The proposed system utilizes a heterogeneous computing platform combining the CPU and FPGA, thereby providing enhanced performance compared to conventional systems. Using a high-definition camera, actual sperm movements could be traced with high fidelity. The FPGA provides very high execution speeds necessary for recursive and expensive image preprocessing tasks. The CPU connects the camera to the devised system and is also responsible for performing calculations. The processing algorithms are executed mainly on the CPU, and the embedded system provides compact structure; moreover, the physical footprint of the system is smaller, which is advantageous in laboratory environments. Consequently, the proposed architecture is considered to be a step ahead of third-generation CASA systems, which is the primary novelty of this work. The proposed semen analysis system is implemented on a real-time operating system (RTOS) that also manages the embedded system with reconfigurable hardware for preprocessing and CPU for processing the executable parts of the algorithm. For sperm detection and tracking, the multiple moving object tracking system (MMOTS) algorithm is developed, which is the second novelty of this work. In terms of object detection, the background subtraction method is used for segmentation in MMOTS. The MMOTS approach is suitable for the selected architecture and imposes less computing load to achieve fast responses, which are crucial for real-time processing. Cellular neural network templates are used and experimented in the preprocessing part of the proposed system, where image-processing steps such as convolution, segmentation, and point representation are applied. This thesis begins with a brief introduction to sperm biology, semen analysis, sperm morphology and motility, and evolution of CASA systems. While the proposed work addresses motility analysis, the developed software also allows segmentation of static images to specify the head, nucleus, mitochondrial part, head shape, and size of the sperm as medical parameters. The algorithms, which are designed by considering computations on heterogeneous computing platforms, was next implemented on recent and state-of-the-art reconfigurable hardware. These hardware are commercially available and contain up-to-date components with reasonable prices. The complete system was realized on an NI CompactRIO 9030 hardware integrated with a microscope and available for medical tests as a prototype. The experimental work on human sperm samples were conducted at the Ege University Faculty of Medicine, İzmir, Turkey and Gelişim Tıp Laboratory, İstanbul, Turkey. The existing CASA system in Gelişim Tıp Laboratory was examined, and some experiments were carried out; the videos of these samples were obtained and examined in our preliminary and simulation studies. With respect to Ege University, the results of the proposed system are compared with those from manual inspections. In the manual assessments, the classifications were performed using the Makler camara (counting chamber), and the results of the proposed system were found to be 100% similar to those from manual assessments. The motility parameters, such as average progressive velocity VAP and curvilinear velocity VCL, can only be estimated roughly from manual assessments, but the results from the proposed system were deemed to be acceptable by the biologists in the laboratory. Experimental investigations were also performed on animal sperm samples at İstanbul University's Faculty of Veterinary Medicine, and the results of the proposed system were found to be approximately 80% compliant with those of the existing system in the laboratory. These variations are attributed to parameter settings; the parameters were roughly set to enable inspection of the effects of parameter values on the results, besides test result comparisons. The proposed system is flexible and hardware-based, so that different biological imaging applications could be deployed on it in the future. Eventually, the goal of this study is to achieve an intelligent biological analysis system.