• Progress in Medical Physics
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• v.26 no.1
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• pp.42-51
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• 2015

In proton therapy, verification of proton dose distribution is important to treat cancer precisely and to enhance patients' safety. To verify proton dose distribution, in a previous study, our team incorporated a vertically-aligned one-dimensional array detection system. We measured 2D prompt-gamma distribution moving the developed detection system in the longitudinal direction and verified similarity between 2D prompt-gamma distribution and 2D proton dose distribution. In the present, we have developed two-dimension prompt-gamma measurement system consisted of a 2D parallel-hole collimator, 2D array-type NaI(Tl) scintillators, and multi-anode PMT (MA-PMT) to measure 2D prompt-gamma distribution in real time. The developed measurement system was tested with $^{22}Na$ (0.511 and 1.275 MeV) and $^{137}Cs$ (0.662 MeV) gamma sources, and the energy resolutions of 0.511, 0.662 and 1.275 MeV were $10.9%{\pm}0.23p%$, $9.8%{\pm}0.18p%$ and $6.4%{\pm}0.24p%$, respectively. Further, the energy resolution of the high gamma energy (3.416 MeV) of double escape peak from Am-Be source was $11.4%{\pm}3.6p%$. To estimate the performance of the developed measurement system, we measured 2D prompt-gamma distribution generated by PMMA phantom irradiated with 45 MeV proton beam of 0.5 nA. As a result of comparing a EBT film result, 2D prompt-gamma distribution measured for $9{\times}10^9$ protons is similar to 2D proton dose distribution. In addition, the 45 MeV estimated beam range by profile distribution of 2D prompt gamma distribution was $17.0{\pm}0.4mm$ and was intimately related with the proton beam range of 17.4 mm.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.33 no.4
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• pp.275-284
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• 1997

This experiment was carried out to measure the sinking depth of each buoy, the change in the net shape of the net, and the tension of sand bag line according to the R (from bag net to the fish court) and L (from fish court to the bag net) current directions and their velocity by the model experiment. The model net was one-fiftieth of the real net, and its size was determined after considering the Tauti’s Similarity Law and the dimension of the experimental tank. 1. The changes of the net shape were as follows : In the current R, the end net of fish court moved 20mm down the lowerward tide and 10mm upper part. So the whole model net moved up at 0.2m/sec. The shape of the net showed an almost linear state from bag net to the fish court at 0.6m/sec. In the current L, the door net moved 242mm down the lowerward tide and 18mm upper part. So the whole model net moved up at 0.2m/sec. The net shape showed an almost linear state from the fish court to the bag net at 0.5m/sec. 2. The sinking depths of each buoy were as follows: In the current R, the head buoy started sinking at 0.2m/sec and sank 20mm, 99mm at 0.3m/sec and 0.6m/sec, respectively. The end buoy didn't sink from 0m/sec to 0.6m/sec but showed a slight quake. In the current L, the end buoy started sinking at 0.1m/sec, and sank 5mm and 108mm at 0.2m/sec and 0.6m/sec, respectively. The whole model net sank at 0.5m/sec except the head buoy. 3. The changes of the sand bag line tension were as follows: In the current R, the tension affected by the sand bag line of the head buoy showed 273.51g at 0.1m/sec increased to 1298.40g at 0.6m/sec. In the current L, the tension affected by the sand bag line of the end buoy on one side showed 137.08g at 0.1m/sec increased to 646.00g at 0.6m/sec. The changes in the sand bag line tension were concentrated on the sand bag line of the upperward tide with increasing velocity at the R and L current directions. However, no significant increase in tension was observed in the other sand bag lines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.