• Progress in Medical Physics
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• v.12 no.1
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• pp.41-50
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• 2001

The quantification analysis of renal scintigraphy is strongly affected by the location, shape and size of region of interest(ROI). When ROIs are drawn manually, these ROIs are not reproducible due to the operators' subjective point of view, and may lead to inconsistent results even if the same data were analyzed. In this study, the effect of the ROI variation on the analysis of renal scintigraphy when the ROIs are drawn manually was investigated, and in order to obtain more consistent results, methods for automated ROI definition were developed and the results from the application of the developed methods were analyzed. Relative renal function, glomerular filtration rate and mean transit time were selected as clinical parameters for the analysis of the effect of ROI and the analysis tools were designed with the programming language of IDL5.2. To obtain renal scintigraphy, $^{99m}$Tc-DTPA was injected to the 11 adults of normal condition and to study the inter-operator variability, 9 researchers executed the analyses. The calculation of threshold using the gradient value of pixels and border tracing technique were used to define renal ROI and then the background ROI and aorta ROI were defined automatically considering anatomical information and pixel value. The automatic methods to define renal ROI were classified to 4 groups according to the exclusion of operator's subjectiveness. These automatic methods reduced the inter-operator variability remarkably in comparison with manual method and proved the effective tool to obtain reasonable and consistent results in analyzing the renal scintigraphy quantitatively.

• The Journal of Korea Robotics Society
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• v.10 no.4
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• pp.223-229
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• 2015

This paper proposes a novel method for detection of hand raising poses from images acquired from a single camera attached to a mobile robot that navigates unknown dynamic environments. Due to unconstrained illumination, a high level of variance in human appearances and unpredictable backgrounds, detecting hand raising gestures from an image acquired from a camera attached to a mobile robot is very challenging. The proposed method first detects faces to determine the region of interest (ROI), and in this ROI, we detect hands by using a HOG-based hand detector. By using the color distribution of the face region, we evaluate each candidate in the detected hand region. To deal with cases of failure in face detection, we also use a HOG-based hand raising pose detector. Unlike other hand raising pose detector systems, we evaluate our algorithm with images acquired from the camera and images obtained from the Internet that contain unknown backgrounds and unconstrained illumination. The level of variance in hand raising poses in these images is very high. Our experiment results show that the proposed method robustly detects hand raising poses in complex backgrounds and unknown lighting conditions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.246-253
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• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• Journal of radiological science and technology
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• v.41 no.5
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• pp.457-462
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• 2018

The purpose of this study was to compare image quality of indirect digital radiography (IDR) system using the International Electro-technical Commission standard (IEC 62220-1), and to suggest the analysis of noise power spectrum (NPS) which were applied to IEC 62220-1 in medical imaging. In this study, Pixium 4600 (Trixell, France) which is indirect flat panel detector (FPD) was used. The size of image receptor (IR) is $7{\times}17$ inch (matrix $3001{\times}3001$) which performed 14bit processing and pixel pitch is $143{\mu}m$. In IEC standard, NPS evaluation were applied to RQA3, RQA5, RQA7 and RQA9. Because of different radiation quality, each region of interesting (ROI) were compared. The results of NPS indicated up to $3.5mm^{-1}$ including low Nyquist frequency. RQA5 indicated the lowest NPS and the others indicated higher NPS results relatively. NPS result of ROI a38 was higher than ROI a92 and this result indicated that there are more noise in left (cathode) than right (anode). This study were to evaluate NPS by using different radiation quality and setting the each ROI, and to suggest the quantitative methods of measuring NPS.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.2
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• pp.131-139
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• 2008

This paper presents a new saliency map which is constructed by providing dynamic weights on individual features in an input image to search ROI(Region Of Interest) or FOA(Focus Of Attention). To construct a saliency map on there is no a priori information, three feature-maps are constructed first which emphasize orientation, color, and intensity of individual pixels, respectively. From feature-maps, conspicuity maps are generated by using the It's algorithm and their information quantities are measured in terms of entropy. Final saliency map is constructed by summing the conspicuity maps weighted with their individual entropies. The prominency of the proposed algorithm has been proved by showing that the ROIs detected by the proposed algorithm in ten different images are similar with those selected by one-hundred person's naked eyes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.393-399
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• 2021

In this study, experiments were conducted to estimate the PM concentrations by learning the nighttime CCTV images of various PM concentrations environments. In the case of daytime images, there have been many related studies, and the various texture and brightness information of images is well expressed, so the information affecting learning is clear. However, nighttime images contain less information than daytime images, and studies using only nighttime images are rare. Therefore, we conducted an experiment combining nighttime images with non-uniform characteristics due to light sources such as vehicles and streetlights and building roofs, building walls, and streetlights with relatively constant light sources as an ROI (Region of Interest). After that, the correlation was analyzed compared to the daytime experiment to see if deep learning-based PM concentrations estimation was possible with nighttime images. As a result of the experiment, the result of roof ROI learning was the highest, and the combined learning model with the entire image showed more improved results. Overall, R² exceeded 0.9, indicating that PM estimation is possible from nighttime CCTV images, and it was calculated that additional combined learning of weather data did not significantly affect the experimental results.

• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.31-40
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• 2017

As 3D cell culture has recently become possible, it has been able to observe a 3D shape of cell and volume. Generally, 3D information of a cell should be observed with a special microscope such as a confocal microscope or an electron microscope. However, a confocal microscope is more expensive than a conventional microscope and takes longer time to capture images. Therefore, there is a need for a method that can reconstruct the 3D shape of cells using a common microscope. In this paper, we propose a method of reconstructing 3D cells using the focus estimator value from multi-focal fluorescence images of cells. Initially, 3D cultured cells are captured with an optical microscope by changing the focus. Then the approximate position of the cells is assigned as ROI (Region Of Interest) using the circular Hough transform in the images. The MSBF (Modified Sliding Band Filter) is applied to the obtained ROI to extract the outlines of the cell clusters, and the focus estimator values are computed based on the extracted outlines. Using the computed focus estimator values and the numerical aperture (NA) of the microscope, we extract the outline of the cell cluster considering the depth and reconstruct the cells into 3D based on the extracted outline. The reconstruction results are examined by comparing with the combined in-focus portions of the cell images.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.27-33
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• 2012

Purpose: Most of diagnosis in the pediatric hydronephrosis patients have been performed $^{99m}Tc$-DMSA renal scan. Then the region of interest (ROI) is set for comparative analysis of uptake ratio in left-right kidney after acquiring the image. But if the equipment set an automatic ROI, the ROI could include expanded renal pelvis due to hydronephrosis and the uptake ratio of left-right kidney will be incorrect result. Therefore this study compared both ROIs including expanded renal pelvis and excluding renal pelvis through experiment using normal kidney phantom and expanded renal pelvis phantom and suggested setting method of improved ROI. In addition, this study have been helped by readout doctor for investigate distinction radiopharmaceutical uptake between renal cortex and remained urine by expanded renal pelvis. Materials and Methods: The both of renal phantoms were filled with water and shacked with $^{99m}TcO_4$ 111 MBq. In order to describe the expanded renal pelvis, the five latex balloon were all filled with 10 mL water and each of balloon was mixed with $^{99m}TcO_4$ 18.5, 37, 55.5, 74, 92.5 MBq. And we made phantom with fixed $^{99m}TcO_4$activity of 37 MBq and mixed water 5, 10, 15, 20, 25 mL in each balloon. The left kidney was fixed its shape and the right kidney was modified like as hydronephrosis kidney by attached the latex balloons. And the acquiring counts were 2 million. After acquisition, we compared the image of ROI with Expanded renal pelvis and the image of ROI without renal pelvis for analyzing difference in the uptake ratio of left-right kidney and for reproducibility, set the ROI 5 times in the same images. Patients were injected $^{99m}Tc$-DMSA 1.5~1.9 MBq/kg and scanned 3 to 4 hours after injection. The each of 3 skillful radio technologists performed the comparing estimation by setting ROI. To determine statistical significance between two data, SPSS (ver. 17) Wilcoxon Signed Ranks Test was used. Results: As a result of renal phantom's experiment, we compared with average of counts Background (BKG) ratios in the setting of ROI including expanded renal pelvis and setting of excluding expanded renal pelvis. Therefore, they can obtain changed counts and changed ratios. Patient also can obtain same results. In addition, the radiopharmaceutical uptake in expanded renal pelvis was come out the remained urine that couldn't descend to ureter by the help of readout doctor. Conclusion: As above results, the case of setting ROI including expanded renal pelvis was more abnormally increasing uptake ratio than the case of setting ROI excluding expanded renal pelvis in analysis the uptake ratio in left-right kidney of hydronephrosis. Because of the work convenience and prompted analysis, the automatic ROI is generally used. But in case of the hydronephrosis study, we should set the manual ROI without expanded renal pelvis for an accurate observation of the uptake ratio of left-right kidney since the radiopharmaceutical uptake in expanded renal pelvis is the remained urine.

• Smart Structures and Systems
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• v.33 no.6
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• pp.399-414
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• 2024

This study discusses the long-term deformation monitoring and shape sensing of bridge girder surfaces with an automated extraction scheme for point clouds in the Region Of Interest (ROI), invariant to the position of a Light Detection And Ranging system (LiDAR). Advanced smart construction necessitates continuous monitoring of the deformation and shape of bridge girders during the construction phase. An automated scheme is proposed for reconstructing geometric model of ROI in the presence of noisy non-stationary background. The proposed scheme involves (i) denoising irrelevant background point clouds using dimensions from the design model, (ii) extracting the outer boundaries of the bridge girder by transforming and processing the point cloud data in a two-dimensional image space, (iii) extracting topology of pre-defined targets using the modified Otsu method, (iv) registering the point clouds to a common reference frame or design coordinate using extracted predefined targets placed outside ROI, and (v) defining the bounding box in the point clouds using corresponding dimensional information of the bridge girder and abutments from the design model. The surface-fitted reconstructed geometric model in the ROI is superposed consistently over a long period to monitor bridge shape and derive deflection during the construction phase, which is highly correlated. The proposed scheme of combining 2D-3D with the design model overcomes the sensitivity of 3D point cloud registration to initial match, which often leads to a local extremum.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.76-82
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• 2012

As the portable camcorder becomes popular, various video stabilization algorithms for de-shaking of camera motion have been developed. In the past, most video stabilization algorithms were based on 2-dimensional camera motion, but recent algorithms show much better performance by considering 3-dimensional camera motion. Among the previous video stabilization algorithms, 3D video stabilization algorithm using content-preserving warps is known as the state-of-the art owing to its superior performance. But, the major demerit of the algorithm is its high computational complexity. So, we present a computationally light full-frame warping algorithm based on ROI (region-of-interest) while providing comparable visual quality to the state-of-the art in terms of ROI. First, a proper ROI with a target depth is chosen for each frame, and full-frame warping based on the selected ROI is applied.