• The Korean Journal of Nuclear Medicine Technology
• /
• v.14 no.1
• /
• pp.35-39
• /
• 2010

Purpose: The aim of this study was to investigate distribution of particle size in phytate kit and compare filtered method with non-filtered method using 200 nm filter for sentinel lymphoscintigraphy (SLS). Materials and Methods: Five phytate kit of having the same available period was measured by particle size analyzer. For in-vivo experiment, $^{99m}Tc$-phytate was injected intradermally at both foot to perform lymphoscintigraphy. Imaging was acquired at 1hour after injection. Region of interest (ROI) was drawn in inguinal and background area for analysis. RAW 264.7 cells (Murine macrophage cell) were prepared for measurement of celluar uptake as a representative of macrophages. Paired t-test was performed using SPSS (SPSS Inc, USA) for statistical analysis. Results: The size of most particle in Techne phytate kit was distributed in 130~650 nm(90.5 %). In-vivo study, the ROI analysis showed similar result between filtered and non-filtered sample, and the numerical value of count/pixel were $58.3{\pm}5.97$ and $60.2{\pm}4.88$. In-vitro study, cellular uptake study also showed no difference between filtered and non-filtered sample by gamma counting. Conclusion: The present study demonstrates that there was no meaning of 200 nm filtered method for SLS using $^{99m}Tc$-phytate.

• The Korean Journal of Nuclear Medicine
• /
• v.27 no.2
• /
• pp.218-222
• /
• 1993

Lung/heart uptake ratio (L/H R) in $^{201}Tl$ myocardial perfusion scan is a reliable marker for long-term prognosis in patients with coronary artery disease. However, the value of L/H R in $^{99m}Tc-MIBI$ myocardial perfusion scan is controversial in determining the prognosis and severity of the coronary artery disease. The purpose of this study was to determine the clinical implications of L/H R in $^{99m}Tc-MIBI$ myocardial perfusion scan. Forty five patients who received $^{99m}Tc-MIBI$ myocardial perfusion scan were divided into control group and coronary artery disease (CAD) group by their clinical findings, EKGs, and $^{99m}Tc-MIBI$ myocardial perfusion scans. Twenty five patients in CAD group were divided into ischemic group and infarct group according to their results from $^{99m}Tc-MIBI$ myocardial perfusion scan. L/H R was calculated on the anterior planar view, 60 minutes after infusion of dipyridamole. Two regions of interest (ROI) were placed on the left lung area 8 pixel above the left ventricle and on the myocardial area which had the highest radioactivity. In the control group, there were no significant differences of L/H R according to sex and age. No significant difference of L/H R was found between the control and CAD group ($0.26{\pm}0.06,\;0.29{\pm}0.05$, p>0.05). In the CAD group, there was also no significant difference of L/H R between the ischemic group and infarct group ($0.29{\pm}0.07,\;0.30{\pm}0.04$, p>0.05). L/H R in CAD group did not show correlations with the defect area of stress polar map (r=0.18, p >0.05) and with the sum of severity weighted extent score or reversibility score which represent severity and extent of myocardial perfusion defect area in stress (r=0.18, p>0.05). We conclude that it is difficult to use L/H R as a marker for severity of CAD in dipyridamole $^{99m}Tc-MIBI$ myocardial perfusion scan.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.2 s.308
• /
• pp.40-47
• /
• 2006

This paper proposes a multitoning method using threshold modulation of MJBNM(Modified Jointly Blue Noise Mask) for banding artifacts reduction. As banding artifacts in multitoning appear as uniform dot distributions around the intermediate output levels, such multitone output results in discontinuity and visually unpleasing patterns in smooth transition regions. Therefore, to reduce these banding artifacts, the proposed method rearranges the dot distribution by introducing pixels in the neighborhood of output levels that occurs banding artifacts. First of all principal cause of banding artifacts are analyzed using mathematical description. Based on this analytical result, a threshold modulation technique of MJBNM which takes account of chrominance error and correlation between channels is applied. The original threshold range of MJBNM is first scaled linearly sot that the minimum and maximum of the scaled range include two pixel more than adjacent two output levels that cover an input value. In an input value is inside the vicinity of any intermediate output levels produce banding artifacts, the output is set to one of neighboring output levels based on the pointwise comparison result according to threshold modulation parameter that determines the dot density and distribution. In this case, adjacent pixels are introduced at the position where the scaled threshold values are located between two output levels and the minimum and maximum threshold values. Otherwise, a conventional multitoning method is applied. As a result, the proposed method effectively decreased the appearance of banding artifacts around the intermediate output levels. To evaluate the quality of the multitone result, HVS-WRMSE according to gray level for gray ramp image and S-CIELAB color difference for color ramp image are compared with other methods.

• Journal of Broadcast Engineering
• /
• v.17 no.5
• /
• pp.756-764
• /
• 2012

In current HEVC, prediction pixels are finally calculated by linear-function interpolation on two reference pixels. It is hard to expect good performance on the case of occurring large difference between two reference pixels. This paper decides more accurate prediction pixel values than current HEVC using linear function. While existing prediction process only uses two reference pixels, proposed method uses DCT-IF. DCT-IF analyses frequency characteristics of more than two reference pixels in frequency domain. And proposed method calculates prediction value adaptively by using linear-function, DCT-IF and cubic-function to decide more accurate interpolation value than to only use linear function. Cubic-function has a steep slope than linear-function. So, using cubic-function is utilized on edge in prediction unit. The complexity of encoder and decoder in HM6.0 has increased 3% and 1%, respectively. BD-rate has decreased 0.4% in luma signal Y, 0.3% in chroma signal U and 0.3% in chroma signal V in average. Through this experiment, proposed adaptive intra prediction method using DCT-IF and cubic-function shows increased performance than HM6.0.

• Journal of the Optical Society of Korea
• /
• v.18 no.4
• /
• pp.317-329
• /
• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1283-1297
• /
• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• The KIPS Transactions:PartB
• /
• v.13B no.4 s.107
• /
• pp.353-360
• /
• 2006

This paper presents a robust algorithm for segmenting a vehicle license plate area from a road image. We consider the features of license plates in three aspects : 1) edges due to the characters in the plate, 2) colors in the plate, and 3) geometric properties of the plate. In the preprocessing step, we compute the thresholds based on each feature to decide whether a pixel is inside a plate or not. A statistical approach is applied to the sample images to compute the thresholds. For a given road image, our algorithm binarizes it by using the thresholds. Then, we select three candidate regions to be a plate by searching the binary image with a moving window. The plate area is selected among the candidates with simple heuristics. This algorithm robustly detects the plate against the transformation or the difference of color intensity of the plate in the input image. Moreover, the preprocessing step requires only a small number of sample images for the statistical processing. The experimental results show that the algorithm has 97.8% of successful segmentation of the plate from 228 input images. Our prototype implementation shows average processing time of 0.676 seconds per image for a set of $1280{\times}960$ images, executed on a 3GHz Pentium4 PC with 512M byte memory.

• Korean Journal of Remote Sensing
• /
• v.27 no.5
• /
• pp.601-611
• /
• 2011

Land cover changes are occurring for a variety of reasons such as urbanization, infrastructure construction, desertification, drought, flood, and so on. Many researchers have studied the cause and effect of land cover changes, and also the methods for change detection. However, most of the detection methods are based on the dichotomy of "change" and "not change" according a threshold value. In this paper, we present a change detection method with the integration of probability, spatial autocorrelation, and hotspot detection. We used the AMOEBA (A Multidirectional Ecotope-Based Algorithm) and developed the AMOEBA-CH (core hotspot) because the original algorithm tends to produce too many clusters. Our method considers the probability of land cover changes and the spatial interactions between each pixel and its neighboring pixels using a local spatial autocorrelation measure. The core hotspots of land cover changes can be delineated by a contiguity-dominance model of our AMOEBA-CH method. We tested our algorithm in a simulation for land cover changes using NDVI (Normalized Difference Vegetation Index) data in South Korea between 2000 and 2008.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.4
• /
• pp.10-21
• /
• 2008

The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.6
• /
• pp.629-635
• /
• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.