• Ocean Science Journal
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• v.40 no.3
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• pp.177-182
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• 2005

The process of embryogenesis and larval development of the asteroid sea star Asterias amurensis $(U{\ddot{u}}tken)$ was observed, with special attention paid to morphological change and larval duration. In reproductive season, mature sea stars were collected under floating net cages, located in Tongyeong, southern Korea. The mature eggs are $138\;{\mu}m$ in average diameter, semi-translucent and orange in color, sperms in good condition appear light cream to white-gray in color. Embryos develop through the holoblastic equal cleavage stage and a wrinkled blastula stage that lasts about 9 hours after fertilization. Gastrulae bearing an expanded archenteron hatch from the fertilization envelope 22 hours after fertilization. At the end of gastrulation, rudiments of the left and right coelom are formed. By day 2, larvae possess complete alimentary canal and begin to feed. At this stage, the larva is called early bipinnaria. In 6-day-old larvae, the pre- and post- oral ciliated bands form complete circuits and the bipinnarial processes start to develop. By day 12, the lateral and anterior projection of the larval wall processes along the ciliated bands begins to thicken and curl, and the ciliated bands become more prominent. By day 32, early brachiolaria are presented with three pairs of brachiolar arms. Advanced brachiolaria with a well-developed brachiolar complex (three pairs of brachia and central adhesive disc) occur 6 weeks after fertilization. In the field, spawning of the sea star was observed in April to May, settlement form larvae and just settlements seem to occur from June to July, and early juveniles occur from August to September. Although we had not described the end of brachiolaria stage, it can be tentatively estimated that the duration of the pelagic stage of A. amurensis is 40 to 50 days.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.14-20
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• 2003

This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

• Applied Microscopy
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• v.38 no.1
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• pp.51-61
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• 2008

This simulation study examined the effect of data acquisition error including the data type of TEM image, and incident beam intensity of the tilt series on 3D tomograms. Simulation was performed with the 3D head phantom model of Kak and Slaney, and the slightly modified 3D head phantom model with enhanced difference in absorption coefficients. Reconstructed tomogram for the original head phantom model using 8-bit gray-scale image was distorted with extremely high level of noise, while an acceptable result was obtained for the modified model. The results for the original model using wrong formulation for the transmitted beam intensity was proved to be incorrect. The high level of noise along the z direction was found in case of the modified model. On the other hand, the wrong value of incident beam intensity in both models gave distorted results. In order to reconstruct an artifacts-free 3D structure from the projections with invisible features in electron tomography, the 16-bit projection images should be used with the correct incident beam intensity which is applied to Beer's law.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

• The Korean Journal of Physiology
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• v.24 no.1
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• pp.181-194
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• 1990

Antidromically activated spinoreticular tract (SRT) cell units in the lumbosacral enlargement of ${\alpha}-chloralose$ anesthetized cats were classified as medial and lateral SRT units according to the location of their axonal termination. Identified SRT units were tested fer antidromic conduction velocity, laterality of their axonal projection, the location in spinal gray, peripheral receptive field, the response pattern to graded mechanichal stimulation and the responsiveness to $A{\delta}$ and C volley of the peripheral nerve. 1) The 59% of 34 medial SRT units were recorded in ipsilateral side to the antidromic stimulation site, but 60% of the 47 lateral SRT units projected to contralateral side. 2) Most of the medial SRT cells and rostral ventrolateral medulla (RVLM)-projecting lateral SRT cells were recorded in lamina VII & VIII. The LRN (lateral reticular nucleus)-projecting SRT cells, however, distributed through all the laminae except superficial ones (I & II). 3) The identified SRT units were classified as low theshold (LT), deep, high threshold (HT), wide dynamic range (WDR) cells, based on the response patterns to graded mechanical stimuli. The proportion of SRT units which receive noxious input was 37.5%, 25% and 75% in the medial, LRN-projecting and RVLM SRT group, respectively. 4) There was no significant difference in the mean conduction velocities between the 3 groups. But the deep cells had significantly higher velocity than that of the HT cells. The above results show that the peripheral inputs to the SRT units are different in the 3 groups: medial, LRN & RVLM SRT group. Especially in case of the SRT cells projecting to RVLM which is a probable candidate fur the integration center of various pressor reflexes such as somatosympathetic reflex, the noxious informations occupy higher proportion of input to them than in other groups. Therefore the noxious information transmitted through the lateral SRT destined for RVLM is expected to play a role in somatosymapthetic reflex.