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Yayın Sperm morphology analysis with CNN based algorithms(IEEE Computer Society, 2014-08-29) Şavkay, Osman Levent; Cesur, Evren; Yalçın, Müştak Erhan; Tavşanoğlu, Ahmet VedatIn this paper Morphological Analysis part of our proposed computer-aided sperm analysis system (CASA) is simulated and the results beside the algorithm steps are presented. The morphology analysis is simply dealing with shape of the sperms and extracting the shape characteristics in medical parameters. The characteristics are obtained by image processing algorithms which utilizes Cellular Nanoscale Network (CNN) based and spatial image processing blocks. The following calculation of medical parameters are obtained from the outputs of image processing blocks. The algorithm is so designed to adapt the final SoC architecture such as Xilinx Zynq7000 device.Yayın Karma CPU + FPGA yapısı üzerinde tasarlanmış bilgisayar destekli sperm analizi sistemi(IEEE, 2015-06-19) Şavkay, Osman Levent; Tavşanoğlu, Ahmet Vedat; Yalçın, Müştak Erhan; Cesur, EvrenBu bildiride karma CPU + FPGA tabanlı bir donanım mimarisi üzerinde tasarlanan Bilgisayar Destekli Semen Analizi (BDSA) sistemi genel özellikleri ile anlatılmıştır. Spermatozoa motilite analizi hareketli çoklu nesne izleme algoritmasıdır, spermatozoa morfoloji analizi için ise ard arda uygulanan çeşitli durağan görüntü işleme yöntemleri ile yapılmaktadır. Sistemimizde kullanılan ve yüksek hız gerektiren hareketli ve durağan görüntü işleme işlevleri için FPGA yapısının paralel işlem yeteneğinden yararlanılmıştır. Çeşitli hesaplamalar ise geliştirilen özel yazılım ile CPU üzerinde gerçeklenmiştir. Biyolojik mikroskoba takılabilen bir HD dijital kamerayı da içermekte olan sistemimizin esnek programlanabilen ve tek başına çalışabilen bir akıllı sistem olarak çalışması da öngörülmüştür.Yayın Realization of preprocessing blocks of CNN based CASA system on FPGA(2013) Şavkay, Osman Levent; Yıldız, Nerhun; Cesur, Evren; Yalçın Müştak, Erhan; Tavşanoğlu, Ahmet VedatIn this paper, hardware optimization of the preprocessing part of a computer aided semen analysis (CASA) system is proposed, which is also implemented on an FPGA device as a working prototype. A real-time cellular neural network (CNN) emulator (RTCNNP-v2) is used for the realization of the image processing algorithms, whose regular, flexible and reconfigurable infrastructure simplifies the prototyping process. For future work, the post-processing part of the CASA system is proposed to be implemented on the same FPGA device as software, using either a soft or hard processor core. By the integration of the pre- and post-processing parts, the designed CASA system will be capable of processing full-HD 1080p@60 (1080×1920) video images in real-time.Yayın Realization of processing blocks of CNN based CASA system on CPU and FPGA(IEEE, 2014) Şavkay, Osman Levent; Cesur, Evren; Yıldız, Nerhun; Yalçın, Mustak Erhan; Tavşanoğlu, Ahmet VedatIn this paper, hardware optimization of the preprocessing and software implementation of the processing blocks of a computer aided semen analysis (CASA) system are proposed, which is also implemented on an FPGA and ARM device as a working prototype. The software implementation of the track initialization, track maintenance, data validation and classification blocks of the processing part are implemented on a Zynq7000 ARM Cortex-A9 processor. In the preprocessing part, a real-time cellular neural network (CNN) emulator (RTCNNP-v2) is used for the realization of the image processing algorithms, whose regular, flexible and reconfigurable infrastructure simplifies the prototyping process. The CASA system introduced in this paper is capable of processing full-HD 1080p@60 (1080 x 1920) video images in real-time.Yayın Computer assisted sperm motility analysis implemented on hybrid CPU+FPGA architecture as an intelligent microscope application(IEEE Computer Society, 2016) Şavkay, Osman Levent; Yalçın, Müştak Erhan; Tavşanoğlu, Ahmet VedatIn this paper we present a Computer Assisted Semen Analysis (CASA) system which is designed and implemented on a hybrid CPU+FPGA architecture platform. The sperm motility analysis deals with the dynamics of sperm movements, thus requires video analysis. A 1280x960 pixel, up to 30 fps programmable camera is used and attached to a trinocular microscope and a PC is used as an HMI and for post calculations, data logging and reporting. The proposed system enables real-time image processing and hence a fast analysis environment, which is important for sperm analysis. In this way we achieved a reconfigurable, reprogrammable, adaptable and extendible system, which can be interpreted as an intelligent microscope.
