• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2540-2545
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• 2015

In this paper, we propose a novel method to extract appendix from ultrasound image automatically to avoid such subjectivity issue. In the process, we apply a series of image processing algorithms such as Ends_in search stretching for emphasizing brightness contrast and binarization, region labelling, and cubic spline interpolation for extracting lower bound fasicia line that is the base of extracting the appendix. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply ART2 algorithm to the organ area in order to extract appendix accurately. Through experiment, the effectiveness of the proposed method is verified based on the field experts' evaluations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2486-2491
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• 2013

There are invisible defects by naked eyes in ceramic material images such as internal stomata, cracks and foreign substances. In this paper we propose a method to detect and extract such defects from ceramic pipe weld zone by applying ART2 learning. In pre-processing, we apply Ends-in Search Stretching to enhance the intensity and then perform fuzzy binarization with triangle type membership function followed by enhanced ART2 that interacts with random input patterns to extract such invisible defects. The experiment verifies that this proposed method is sufficiently effective.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.341-346
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• 2012

In rehabilitation where ultrasonographic diagnosis is not popular, it could be subjective by medical expert's experience. Thus, it is necessary to develop an objective automative procedure in ultrasonic image analysis. A disadvantage of existing automative analytic procedure in musculoskeletal system is to designate an incorrect muscle area when the figure of fascia is vague. In this study, we propose a new procedure to extract more accurate muscle area in abdomen ultrasonic image for that purpose. After removing unnecessary noise from input image, we apply End-in Search algorithm to enhance the contrast between fascia and muscle area. Then after extracting initial muscle area by Up-Down search, we trace the fascia area with a mask based on morphological and directional information. By this tracing of mask movements, we can emphasize the fascia area to extract more accurate muscle area in result. This new procedure is proven to be more effective than existing methods in experiment using convex ultrasound images that are used in real world rehabilitation diagnosis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.94-96
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• 2013

본 논문에서는 복부 초음파 영상에서 Ends_in Search Stretching 기법을 적용하여 명암 대비를 강조한 후, 이진화, 영역 레이블링 기법, 잡음 제거를 통해 근막을 추출하고, 근막 영역의 하단 경계선을 기준으로 Cubic Spline 보간법을 적용하여 복부 근육의 근막 하단 영역을 추출한다. 복부 초음파 영상에서 추출된 근막 하단 영역을 이용하여 근막 영역을 제거한 후, 거리 기반 퍼지 ART 알고리즘을 적용하여 충수 후보 영역을 추출한 다. 추출된 충수 후보 영역에 침식 연산과 영역 레이블링 기법을 적용하여 충수를 추출한다. 제안된 방법을 복부 초음파 영상을 대상으로 실험한 결과, 기존의 충수 추출 방법보다 객관적이고 효율적으로 충수와 소장의 명암도 차이를 구별할 수 있어 충수 영역이 이전의 방법 보다 비교적 정확히 추출되는 것을 영상의학과 관련 전문의를 통해 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.413-416
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• 2010

본 논문에서는 요부 영상에서 근육을 추출하는 방법을 제안한다. 제안된 방법은 초음파 영상에서 왜곡이 존재하지 않는 영역을 측정 할 근육 영역을 설정한 후, 초기 초음파 영상에서 불필요한 잡음을 제거하고 Ends-in Search Stretching 기법을 적용하여 근육 영역의 명암 대비를 강조한다. 그리고 형태학적 특징을 이용하여 등뼈 영역과 피하지방을 분리한 후, 4 방향 윤곽선 추적 알고리즘을 적용하여 피하지방의 하단 부분을 추출한다. 또한 최대 및 최소 명암도를 조정하여 얻어진 등뼈의 후보 영역에서 형태학적 특징을 이용하여 잡음을 제거하고 최종적으로 등뼈 영역을 추출한다. 추출된 등뼈 영역에 대해 피하지방층과 등뼈 사이를 근육의 두께로 측정한다. 본 연구에서 제안된 방법을 요부의 초음파 영상에 적용하여 근육 영역을 추출한 결과, 제안된 방법이 초음파 영상에서 근육 영역들의 두께를 측정하는데 기존의 근육 측정 방법보다 효과적인 것을 확인 할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.87-89
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• 2013

본 논문에서는 Ends-In Search Stretching 기법을 적용하여 명암 대비를 강조하고, 명암 대비가 강조된 영상에서 삼각형 타입의 소속 함수를 이용한 퍼지 이진화 기법을 적용한 후, 그 다음 임의의 패턴 입력에 대해서도 효과적으로 특징을 분류하는 개선된 ART2 알고리즘을 이용하여 결함 영역을 검출한다. 제안된 방법을 세라믹 소재 영상을 대상으로 실험한 결과, 기존의 방법보다 효율적으로 결함이 검출되는 것을 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.55-60
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• 2011

In this paper, we propose a new method for extracting muscles from lumbar images. The proposed method sets areas without distortions with field expert's assistance as areas of measuring interest and removing noises from initial ultrasonic videos. Then, the method emphasizes the brightness contrast with Ends-in search stretching algorithm and separate thoracic vertebra from subcutaneous fat area using morphological characteristics. 4-directions contour tracing algorithm is applied to extract the bottom of subcutaneous fat area. Extracting thoracic vertebra area requires noise removal and morphological characteristics as well among candidate areas obtained by controlling min-max brightness. The thickness of muscles is then defined as the length between subcutaneous fat area and extracted thoracic vertebra. The experiment which consists of 368 image analysis verifies that the proposed method is more effective in measuring the thickness of muscles than before.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2321-2326
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• 2011

Cervical vertebrae are a complex structure and an important part of human body connecting the head and the trunk. In this paper, we propose a method to extract sternocleidomastoid muscle from ultrasonography images of cervical vertabrae automatically. In our method, Region of Interests(ROI) is extracted first from an ultrasonography image after removing unnecessary auxiliary information such as metrics. Then we apply Ends-in search stretching algorithm in order to enhance the contrast of brightness. Average binarization is then applied to those pixels which its brightness is sufficiently large. The noise part is removed by image processing algorithms. After extracting fascia encloses sternocleidomastoid muscle, target muscle object is extracted using the location information of fascia according to the number of objects in the fascia. When only one object is to be extracted, we search downward first to extract the target muscle area and then search from right to left to extract the area and merge them. If there are two target objects, we extract first from the upper-bound of higher object to the lower-bound of lower object and then remove the fascia of the target object area. Smearing technique is used to restore possible loss of the fat area in the process. The thickness of sternocleidomastoid muscle is then calculated as the maximum thickness of those extracted objects. In this experiment with 30 real world ultrasonography images, the proposed method verified its efficacy and accuracy by health professionals.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1309-1316
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• 2016

This paper suggests adjusting TCP for smartphones that often have small size data transmission tendency. Usage of smartphones has been risen dramatically in recent years, including frequent usage of real-time map search, public transportation search, online games, and SNS. Because the small size data transmission ends before the phase of the TCP congestion avoidance, this paper suggests an algorithm that increases the transmission speed ahead of the traffic congestion event. The algorithm reduces unnecessary delay by data size-driven adjustment of the Linux Quick ACK and Nagle's algorithm. Therefore, TCP is improved to maintain a high transmission rate steadily in small data transmission.

• 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.