• Journal of the Korean Society of Radiology
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• v.10 no.4
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• pp.247-253
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• 2016

Ultrasound devices diagnose many disease, which is widely used, can not be standardized quantitative evaluated in order to evaluate sonography image of quality. Therefore, in this papers, aims to get correct image in order to accurate diagnosis by figuring out the appropriate parameter based on each target by measuring distortion which results in the analyzation of the sensitivity of SNR and the histogram of signal by manipulating parameter of 8 mm target in ATS-539 multipurpose phantom. Equipment using Acuson sequoia 512, convex probe and utilizes multi-objective phantom. experiment method is that first you put the phantom on the flat and acquire 85 sheets of image, changing frequency(2,3,4 MHz, harmonic 3, 4, 4.5 MHz), Focus(2, 4, 6 unit), and Dynamic Range(58, 68, 78, 88, 98) for a 8 mm structure. through the Image J program. The sensitivity angle of 8mm target through Image J program is gauged by each separate target SNR and the distorted angle subtract and measure Histogram of background from Histogram of signal and take top 40% from the given result value above. According to parameter variation we found out proper parameter by acquiring SNR of sensitivity and distortion data for aspect of transition. The more this findings have Focus, the lower distortion value and at 4 MHz frequency this result have high SNR and low distortion value. Dynamic Range got an appropriate image on 88 and 98. It is considered on the basis of the experimental data, the probability of disease diagnosis will get higher.

• Journal of Veterinary Clinics
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• v.22 no.3
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• pp.186-189
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• 2005

For the assessment of the clinical application of histogram on internal parenchymal organs, ultrasonography with a multi-frequency transducer was taken. We scanned in the region of right cranial abdomen for both liver and right kidney, and left cranial abdomen for liver, spleen and left kidney in 9 normal Beagle dogs. The data from histogram examined in a region of interest centered on each picture element of B-mode images at the same depth were compared among liver, renal cortex, spleen, cortex and medulla of each kidney. The right renal cortex showed significantly lower echogenicity than parenchyma of liver by $15{\%}$. Spleen was more echogenic than the cortex of the left kidney by $23{\%}$, and liver was more echogenic than the left renal cortex by $30{\%}$. Renal cortex was more echogenic than medulla by $47{\%}$ and $65{\%}$ on the right and left side, respectively (p<0.05). The mean (${\pm}SD$) values calculated echogenicity were $46.2{\pm}12.3\;(95\%$ confidential interval (CI), 41.0 to 55.0) and $53.4{\pm}12.1\;(95\%$ CI, 47.0 to 55.1) in in the right renal cortex and liver parenchyma, $65.0{\pm}11.8\;(95\%$ CI, 57.9 to 71.0) and $51.0{\pm}16.9\;(95\%$ CI, 42.8 to 54.1) in splenic parenchyma and renal cortex. And the mean values calculated echogenicity were $65.0{\pm}10.15\;(95\%$ CI, 60.1 to 71.5) and $52.0{\pm}9.4\;(95\$ CI, 43.8 to 60.3) in liver parenchyma and the left renal cortex, $54.5{\pm}18.3\;(95\%$ CI, 40.1 to 62.8) and $35.0{\pm}16.2\;(95\%$ CI, 24.2 to 43.6) in the left renal cortex and medulla. And the mean values calculated echogenicity were $55.0{\pm}14.4\;(95\%$ CI, 47.3 to 61.7) and $40.0{\pm}13.2\;(95\%$ CI, 34.3 to 46.7) in the right renal cortex and medulla, respectively. In addition, the echogenicity ratios were $0.86{\pm}0.11$ between the right renal cortex and liver parenchyma, $1.37{\pm}0.47$ between spleenic parenchyma and the left renal cortex, $1.30{\pm}0.19$ between liver parenchyma and the left renal cortex. All the values measured showed significant different (p<0.05). Ultrasound histogram is simple, useful and feasible to evaluate the sonographic architecture of the internal organs such as liver, spleen and kidney, quantitatively.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1296-1301
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• 2008

Ultrasonography constructs pictures of areas inside the body needs in diagnosis by bouncing high-enorgy sound waves(ultrasound) off internal tissues or organs. In constructing an ultrasonographic image, the weakness of bounding signals induces noises and detailed differences of brightness, so that having a difficulty in detecting and diagnosing with the naked eyes in the analysis of ultrasonogram. Especially, the difficulty is extended when diagnosing muscle areas by using ultrasonographic images in the musculoskeletal test. In this paper, we propose a novel image processing method that computationally extracts a muscle area from an ultrasonographic image to assist in diagnosis. An ultrasonographic image consists of areas corresponding to various tissues and internal organs. The proposed method, based on features of intensity distribution, morphology and size of each area, extracts areas of the fascia, the subcutaneous fat and other internal organs, and then extracts a muscle area enclosed by areas of the fascia. In the extraction of areas of the fascia, a series of image processing methods such as histogram stretching, multiple operation, binarization and area connection by labeling is applied. A muscle area is extracted by using features on relative position and morphology of areas for the fascia and muscle areas. The performance evaluation using real ultrasonographic images and specialists' analysis show that the proposed method is able to extract target areas being approximate to real muscle areas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.186-188
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• 2021

본 논문에서는 초음파 영상에서 DBSCAN(Density-based spatial clustering of applications with noise)과 FCM 클러스터링 기반 양자화 기법을 적용하여 결절종을 추출하는 방법을 제안한다. 본 논문에서는 초음파 영상 촬영 시 좌우 상단의 지방층 영역과 하단 영역의 명암도가 어두운 영역을 잡음 영역으로 설정한다. 그리고 초음파 영상에 퍼지스트레칭 기법을 적용하여 잡음 영역을 최대한 제거 한 후에 ROI 영역을 추출한다. 추출된 ROI 영역에서 밀도 분포를 분석하기 위하여 히스토그램을 분석한 후에 DBSCAN을 적용하여 초음파 영상에서 결절종 후보에 해당되는 명암도를 추출한다. 추출한 후보 명암도를 대상으로 FCM 클러스터링 기법을 적용한다. FCM을 적용하는 단계에서 결절종의 저에코 혹은 무에코의 특징을 이용하여 클러스터 중심 값이 가장 낮은 클러스터를 양자화 한 후에 라벨링 기법을 적용시켜 결절종의 후보 객체를 추출한다. 제안된 결절종 추출 방법의 성능을 분석하기 위해 전문의가 결절종 영역을 표기한 초음파 영상과 표기되지 않은 초음파 영상 120쌍을 대상으로 DBSCAN, FCM, 그리고 제안된 방법 간의 성능을 비교 분석하였다. 제안된 방법에서는 120개의 초음파 영상에서 106개 결절종 영역이 추출되었고 FCM 기법에서는 80개가 추출되었고 DBSCAN에서는 36개가 추출되었다. 따라서 제안된 방법이 결절종 추출에 효율적인 것을 확인하였다.

• Journal of radiological science and technology
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• v.47 no.1
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• pp.29-37
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• 2024

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.398-403
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• 2007

초음파 영상은 미세한 명암도 차이 등에 의해 분석 과정에서 근육 영역의 위치와 크기를 판단하는데 어려움이 발생하고 이로 인해 근육 영역을 파악하는데 주관성이 개입된다. 본 논문에서는 근육영역을 객관적으로 분석하기 위해 ART2 신경망을 적용하여 양자화를 수행한 후, 국부적 영역에서 근육 영역을 추출한다. 초음파 영상에서 히스토그램 평활화와 엔드인 탐색 알고리즘을 적용하여 명암도의 분포와 밝기 값을 보정 한 후, ART2 신경망을 이용하여 유사한 영역을 클러스터링 한다. 그리고 클러스터링 된 각 영역의 크기, 위치 및 명암도 정보를 분석하여 피하지방, 근육 막, 기타 배경 영역으로 분류한다. 최종적인 근육 영역을 찾기 위해 근육 막 내부 객체들 간의 거리, 각도를 이용하여 근육 막 영역에 둘러싸인 근육 영역을 추출한다. 실제 초음파 영상을 대상으로 실험한 결과, 일반적인 클러스터링 기법을 적용한 방법 보다 ART2 기반 양자화와 제안된 영역 확장 기법으로 근육영역을 추출하고 분석하는 것이 효율적임을 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.11-17
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• 2014

While safe and convenient, ultrasound imaging analysis is often criticized by its subjective decision making nature by field experts in analyzing musculoskeletal system. In this paper, we propose a new automatic method to extract muscle area using ART2 neural network based quantization. A series of image processing algorithms such as histogram smoothing and End-in search stretching are applied in pre-processing phase to remove noises effectively. Muscle areas are extracted by considering various morphological features and corresponding analysis. In experiment, our ART2 based Quantization is verified as more effective than other general quantization methods.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.91-98
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• 2011

This paper proposes an efficient and accurate vision based recognition and tracking framework for texture free objects. We approached this problem with a two phased algorithm: detection phase and tracking phase. In the detection phase, the algorithm extracts shape context descriptors that used for classifying objects into predetermined interesting targets. Later on, the matching result is further refined by a minimization technique. In the tracking phase, we resorted to meanshift tracking algorithm based on Bhattacharyya coefficient measurement. In summary, the contributions of our methods for the underwater robot vision are four folds: 1) Our method can deal with camera motion and scale changes of objects in underwater environment; 2) It is inexpensive vision based recognition algorithm; 3) The advantage of shape based method compared to a distinct feature point based method (SIFT) in the underwater environment with possible turbidity variation; 4) We made a quantitative comparison of our method with a few other well-known methods. The result is quite promising for the map based underwater SLAM task which is the goal of our research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.59-62
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• 2021

본 논문에서는 사다리꼴 형태의 소속 함수를 적용하여 스트레칭 하는 과정에서 상한과 하한을 FCM 기반 양자화 기법을 적용하여 동적으로 조정하는 퍼지 스트레칭 기법을 제안한다. 제안된 퍼지 스트레칭 기법은 FCM 기반 양자화 기법을 적용하여 각 클러스터를 생성하고 생성된 각 클러스터의 중심에 해당되는 명암도를 이용하여 사다리꼴 형태의 소속 함수의 구간을 설정한다. 그리고 설정한 구간 정보를 이용하여 스트레칭을 위한 상한과 하한을 구하여 영상을 스트레칭 한다. 제안된 FCM 양자화 기반 퍼지 스트레칭 기법의 성능을 분석하기 위해서 명암도 분포가 좁고 명암 대비가 낮은 결절종 초음파 영상과 컨테이너 영상을 대상으로 실험하였다. 실험 결과에서도 알 수 있듯이 기존의 히스토그램 스트레칭 기법과 삼각형 형태의 소속 함수를 적용한 퍼지 스트레칭 기법보다 명암 대비가 향상되었다. 결절종 초음파 영상에서는 결절종 영역과 그 외의 영역 간의 명암 대비가 뚜렷하게 나타나서 결절종 추출에 효과적인 것을 확인하였고 컨테이너 영상에서는 컨테이너 데미지를 추출하는데 필요한 컨테이너 굴곡선 등과 같은 특징이 다른 기법들에 비해 선명하게 나타났다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.515-522
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• 2021

In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.