• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.31-39
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• 2010

In some image processing system or the users who want to see a specific region of image simply, if a part of the image has higher quality than other regions, it would be a nice service. Specifically in mobile environments, preferential service was needed, as the screen size is small. So, JPEG2000 supplies this function. But this doesn't support the process to extract specific regions or service and does the functions to add some techniques. It is called by ROI(Region-of-Interest). In this paper, we use images including human faces, which are processed most preferentially and compressed with high quality. Before an image is served to the users, it is compressed and saved. Here, the face parts are compressed with higher quality than the background which are relatively with lower quality. This technique can offer better service with preferential transferring of the faces, too. Besides, whole regions of the image are compressed with same quality and after searching the faces, they can be preferentially transferred. In this paper, we use a face extraction approach based on neural network and the preferential processing with EBCOT of JPEG2000. For experimentation, we use images having several human faces and evaluate objectively and subjectively, and proved that this approach is a nice one.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2206-2212
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• 2013

We propose a method for the classification of fatty liver by ultrasound imaging using Fuzzy Contrast Enhancement Technique and FCM. ROI images are extracted after removal of information data except ultrasound image of the liver and the kidney then image contrast is improved by Fuzzy Contrast Enhancement Algorithm. The images applied Fuzzy Contrast Enhancement Technique is applied average binarization then ROI images of liver and kidney parenchyma are extracted using Blob algorithm. Representative brightness is extracted in the liver and kidney images using the most frequent brightness level after classification of 10 brightness levels. We applied this method to ultrasound images and a radiologist confirmed the accuracy of diagnosis for fatty liver. This method would be a model for automatic method in the diagnosis of fatty liver.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.55-62
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• 2021

In this paper, we propose a human-content interface that allows users to quickly and efficiently search data through friction-based scrolling with ROI(Regions of interests). Our approach, conceived from the behavior of finding information or content of interest to users, efficiently calculates ROI for a given content. Based on the kernel developed by conceiving from GMM(Gaussian mixture model), information is searched by moving the screen smoothly and quickly to the location of the information of interest to the user. In this paper, linear interpolation is applied to make one softer inertia, and this is applied to scrolls. As a result, unlike the existing approach in which information is searched according to the user's input, our method can more easily and intuitively find information or content that the user is interested in through friction-based scrolling. For this reason, the user can save search time.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.151-158
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• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.73-80
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• 2004

In this paper, we propose a medical image processing method for detection of periodontal disease. The proposed method is the method of an automatic image alignment and detection of minute changes, to overcome defects in the conventional subtraction radiography by digital image processing technique, that is necessary for getting subtraction image and ROI(Region of Interest) focused on a selection method using the structured features in target images. And the method services accuracy, consistency and objective information or data to results. In result, easily and visually we can identify minute differences in the affected parts whether they have problems or not, and using application system.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.153-156
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• 2003

In this paper, we propose a new scheme for automatic segmentation of the liver in CT images. The proposed scheme is carried out on region of interest(ROI) blocks that include regions of the liver with high probabilities. The ROI approach saves unnecessary computational loss in finding the accurate boundary of the liver. The proposed method utilizes the composition of multi-size morphological filters with a prior knowledge, such as the general location or the approximate intensity of the liver to detect the initial boundary of the liver. Then, we make the gradient image with the weight of the initial liver boundary and segment the liver legion by using an immersion-based waters hed algorithm in the gradient image. finally, the refining process is carried out to acquire a more accurate liver region.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1309-1311
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• 2013

본 연구에서는 의료 진단키트의 자동판독 시스템에서, 통제되지 않은 조명 환경에서도 정확한 색상 판별을 위한 ROI 영역 추출 기법과 조명 보정 기법을 고찰한다. 3단계로 세분된 ROI 추출 과정은 조명변화에 적응적인 배경영상 정보를 유지하고, 노이즈 제거와 에지 추출 과정을 포함한다. 진단 결과의 정량적 판별에 중요 지표가 되는 색상정보가 조명의 영향의 의해 왜곡되는 것을 보완하기 위하여 표본 추출된 학습데이터로부터 조명 보정 곡선을 생성한다. 20종류의 색상패턴을 대상으로 적용한 실험 결과를 통하여 제안된 이론의 유용성을 고찰한다.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• 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.