• Clinical and Experimental Reproductive Medicine
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• 제24권3호
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• pp.335-340
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• 1997

Fluorescence in situ hybridization (FISH) techniques allow the enumeration of chromosome abnormalities and from a great potential for many clinical applications. In order to produce quantitative and reproducible results, expensive tools such as a cooled CCD camera and a computer software are required. We have developed a Chromosome Image Processing System (Chips) using FISH that allows the detection and mapping of the genetic aberrations. The aim of our study, therefore, is to evaluate the capabilities of our original system using a black-and-white video camera. As a model system, three repetitive DNA probes (D18Z1, DXZ1, and DYZ3) were hybridized to variety different clinical samples such as human metaphase spreads and interphase nuclei obtained from uncultured peripheral blood lymphocytes, uncultured amniocytes, and germ cells. The visualization of the FISH signals was performed using our system for image acquisition and pseudocoloring. FISH images were obtained by combining images from each of probes and DAPI counterstain captured separately. Using our original system, the aberrations of single or multiple chromosomes in a single hybridization experiment using chromosomes and interphase nuclei from a variety of cell types, including lymphocytes, amniocytes, sperm, and biopsied blastomeres, were enabled to evaluate. There were no differences in the image quality in accordance with FISH method, fluorochrome types, or different clinical samples. Always bright signals were detected using our system. Our system also yielded constant results. Our Chips would permit a level of performance of FISH analysis on metaphase chromosomes and interphase nuclei with unparalleled capabilities. Thus, it would be useful for clinical purposes.

• Tunnel and Underground Space
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• 제33권4호
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.