• 핵의학기술
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• 제16권2호
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• pp.99-105
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• 2012

Purpose : This study is designed to compare two parameters reflecting $^{18}F$-FDG uptake, SUV and radioactivity, for diagnosis of thyroid cancer in dual time $^{18}F$-FDG PET/CT imaging and to find which parameter is more useful to decide whether the tumor is malignant or not. Materials and Methods : We performed retrospective study for 40 patients. All patients are diagnosed as primary thyroid cancer and examined $^{18}F$-FDG PET/CT. First, we got the dispersion of scattering beam of neck and lung apex to set a background and compared each dispersion, mean value, standard deviation of maxSUV and radioactivity. Also, mean maxSUV, ${\Delta}maxSUV$, ${\Delta}maxBq$/ml(%) and radioactivity between groups according to lesion's size based on biopsy are compared with independent-sample t-test. Results : the values that were from maxSUV and radioactivity measurement technique were compensated and calculated to practical values for mean comparison and patients were divided to two groups based on tumor size, Group1 ($size{\leq}1$ cm, n=21), Group2 (size>1 cm, n=19) for accurate comparison. In Group1, maxSUV (semi-quantitative analysis) was increased from $5.64{\pm}5.85$ (1.89~17.84) at first image to $5.90{\pm}5.01$ (1.95~18.22) at second image and radioactivity (Bq/ml) (quantitative analysis) showed similar increase from $5.93{\pm}6.38$ (2.50~16.75) at first image to $6.01{\pm}5.25$ (2.66~16.58) at second image. In Group2, TFmaxSUV was $10.54{\pm}14.36$ (2.54~33.89) in true first image, TSmaxSUV was $9.85{\pm}12.88$ (2.62~26.20) in true second image separately. The maxSUV showed a significant difference in the mean comparison between the two groups (p=0.035) But, mean radioactivity (Bq/ml) was $5.93{\pm}6.38$ (4.81~40.99) in true first image, $6.01{\pm}5.25$ (4.51~36.93) in true second image and didn't show a significant difference statistically (p=0.126) Conclusion : In diagnosis of thyroid tumor, SUV and radioactivity depending on $^{18}F$-FDG uptake showed high similarity with coefficient of determination (R2=0.939) and malignant evaluation results using dual time also showed similar aspect. Radioactivity for evaluation of malignant tumor didn't show better specificity or sensitivity than maxSUV.

• 대한두경부종양학회:학술대회논문집
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• 대한두경부종양학회 1999년도 춘계 학술대회
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• pp.99-102
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• 1999

• 대한치과의사협회지
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• 제42권2호통권417호
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• pp.76-83
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• 2004

• Tribology and Lubricants
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• 제13권1호
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• pp.1-7
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• 1997

• 융합신호처리학회논문지
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• 제16권3호
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• pp.96-101
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• 2015

본 논문에서는 한방 의료의 설진에 있어서 객관적인 진단 지표의 생성을 위해 자외선 혀 영상 채널 분석과 설태 검출에 의한 WTCI(Winkel Tongue Coating Index) 설태 평가 방법을 제안한다. 제안한 방법은 설태 영역 검출을 위하여 자외선 광원에 의해 생성된 혀 영상의 칼라 모델별 각 색상 채널의 히스토그램을 분석한다. 그리고 선택된 혀 영상 채널을 이용하여 설태 검출에서의 성능 검증 실험을 수행한다. 또한 WTCI 설태 지표 생성을 위한 테스트 샘플과 실영상 검증 실험을 실시하여 설진 지표의 객관성을 검증한다. 제안한 컴퓨터 지원 WTCI 설태 평가 방법의 성능 평가를 위해서 샘플 영상을 이용하여 계산의 정확성을 검증하고, 다양한 실제 피실험자의 혀 영상에 적용한 결과 성공적인 결과를 보였다.

• 대한의용생체공학회:의공학회지
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• 제28권2호
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• pp.294-299
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• 2007

For efficient and accurate diagnosis of ultrasound images, appropriate time gain compensation(TGC) and dynamic range(DR) control of ultrasound echo signals are important. TGC is used for compensating the attenuation of ultrasound echo signals along the depth, and DR controls the image contrast. In recent ultrasound systems, these two factors are automatically set by a system and/or manually adjusted by an operator to obtain the desired image quality on the screen. In this paper, we propose an algorithm to find the optimized parameter values far TGC and DR automatically. In TGC optimization, we determine the degree of attenuation compensation along the depth by dividing an image into vertical strips and reliably estimating the attenuation characteristic of ultrasound signals. For DR optimization, we define a novel cost function by properly using the characteristics of ultrasound images. We obtain experimental results by applying the proposed algorithm to a real ultrasound(US) imaging system. The results verify that the proposed algorithm automatically sets values of TGC and DR in real-time such that the subjective quality of the enhanced ultrasound images may be sufficiently high for efficient and accurate diagnosis.

• 한국항행학회논문지
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• 제24권2호
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• pp.148-154
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• 2020

주름은 피부의 노화도를 알 수 있는 주요한 특징 중의 하나이다. 기존의 영상처리기반 주름검출은 다양한 피부 영상에 효과적으로 대처하기 어렵다. 특히, 주름이 선명하지 않고 주변 피부와 유사한 경우 주름추출 성능은 급격히 떨어진다. 본 논문에서는 현미경 피부 영상에서 주름추출을 위해 딥러닝을 적용한다. 일반적으로 현미경 영상은 광각렌즈를 탑재하므로 영상 가장자리 영역의 밝기가 어둡다. 본 논문에서는 이를 해결하기 위해 피부 영상의 밝기를 추정하여 보정 한다. 또한, 주름추출에 적합한 의미분할 네트워크의 구조를 적용한다. 제안방법은 연구실에서 수집한 피부 영상에 대한 테스트 실험에서 99.6%의 정확도를 획득하였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 추계학술대회
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• pp.554-556
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• 2011

각종 폐혈관 질환의 발생에 따른 정확하고 빠른 진단의 필요성이 강조되었다. 몇 가지 폐혈관 조영술의 제약사항의 존재로 흉부 CT에 대한 영상 처리의 필요성을 인지하였고 의료 영상처리의 다양성을 위해 ITK를 이용한 폐혈관 분할을 제안하였다. 본 논문은 명암 값을 기반한 방법으로 두 단계의 폐 영역 분할과 혈관 분할의 과정을 수행한다. 각 단계로 폐 영역 분할은 영상 향상, 문턱치 값, 관심영역 잘라내기로 결과 영상을 획득하고 폐 혈관 분할은 획득된 폐 영역에 영역 채우기를 적용하여 얻는다. 분할된 폐혈관 영상을 바탕으로 3차원 시각화 영상을 획득하여 폐혈관에 대한 다양한 관점의 분석 및 진단이 가능할 것으로 판단된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.