• International Journal of Vascular Biomedical Engineering
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• 제4권1호
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• pp.27-30
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• 2006

Background; Pleural micro-metastasis of lung cancer is detected by touch print cytology or pleural lavage cytology, but its prognostic impact has not elucidated yet. We hypothesize that recurrence may depend on the amount of tumor cells disseminated in pleural cavity, if the invasiveness of all cancer is the same. To predict the amount of tumor cells disseminated in pleural cavity, we need pleural surface area, distributed pattern of cells and concentration of cells per unit area. Human pleural surface area has not reported yet. In this report, we calculate the normal human pleural surface area using CT image data processing. Methods; Twenty persons were checked CT scan, and we obtained the data from each image. In order to calculate the pleural surface, the outline of lung was firstly extruded from CT image data using home-made Digitizer program. And the distance between CT images was calculated from the extruded outline. Finally a normal human pleural surface was calculated from function between the distance of consecutive CT images and the calculated length. Results; Their mean age is $65{\pm}12$ years old (range $26{\sim}77$), body weight is $62{\pm}9\;kg\;(48{\sim}80)$, and height is $167{\pm}6\;cm\;(156{\sim}176)$. The number of images used is $36{\pm}7\;(24{\sim}51)$. Pleural surface area is $211,888{\pm}35,756\;mm^2\;(143,880{\sim}279,576)$. Right-side pleural surface area is $107,932\;mm^2$ and Lt is $103,955\;mm^2$. Costal, mediastinal and diaphragmatic surfaces of right-side pleura are $77,483\;mm^2,\;39,057\;mm^2,\;and\;8,608\;mm^2$ respectively, and left-side are $72,497\;mm^2,\;35,578\;mm^2,\;and\;4,120\;mm^2$ respectively. Conclusion; Normal human pleural surface area is calculated using CT image data at first and the result is about $0.212\;m^2$.

• 대한핵의학회지
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• 제17권1호
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• pp.33-39
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• 1983

Sixty-nine patients with diffuse lung uptake of $^{99m}Tc-tin$ colloid were evaluated to determine the kinds of associated diseases, the incidence of associated liver scan abnormalities, and prognosis. The results were as follows: 1) Diseases associated with diffuse lung uptake included malignancies in 31 patients, infectious diseases in 19, chronic liver diseases in 14, and others in 5. It appeared that the marked degree of lung uptake was associated with severe diseases. 2) Thirty-one of the 69 patients(45%) had abnormal liver size, 43(62%) had space occupying lesions or nonhomogeneity in liver image, 37(54%) had splenomegaly and 45(65%) had increased splenic uptake. Increased bone marrow uptake was found in 48(70%) and renal uptake in 15(22%). As the degree of lung uptake increased, there was a statistically significant (p<0.05) tendency for the incidences of the abnormal liver image and renal uptake to increase. 3) Sixty-two of the 69 patients were followed up for one to 439 days(mean 44 days) after liver scanning. Eleven(18%) were dead, 10(16%) were aggravated, and 13(21%) were improved. Most of improved patients had infectious diseases. It appeared that diffuse lung uptake of $^{99m}Tc-Tn$ colloid was found in the various diseases including malignancies, infections, and chronic liver diseases, and that it was strongly associated with other liver scan abnormalities, but was not necessarily associated with a poor prognosis, particularly when underlying diseases were infections.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권2호
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• pp.137-142
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• 2012

The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.

• 대한방사선기술학회지:방사선기술과학
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• 제44권6호
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• pp.635-643
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• 2021

The results of empirical researches on the diagnosis of lung cancer are insufficient, so it is limited to objectively judge the clinical possibility and utilization according to the accuracy of diagnosis. Thus, this study retrospectively analyzed the lung cancer diagnostic performance of PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) by using the decision matrix. This study selected and experimented total 165 patients who received both hematological CEA (Carcinoembryonic Antigen) test and hybrid PET-MRI (¹⁸F-FDG, 5.18 MBq/kg / Body TIM coil. VIVE-Dixon). After setting up the result of CEA (positive:>4 ㎍/ℓ. negative:<2.5㎍/ℓ) as golden data, the lung cancer was found in the image of PET-MRI, and then the SUV_max (positive:>4, negative:<1.5) was measured, and then evaluated the correlation and significance of results of relative diagnostic performance of PET-MRI compared to CEA through the statistical verification (t-test, P>0.05). Through this, the PET-MRI was analyzed as 96.29% of sensitivity, 95.23% of specificity, 3.70% of false negative rate, 4.76% of false positive rate, and 95.75% of accuracy. The false negative rate was 1.06% lower than the false positive rate. The PET-MRI that significant accuracy of diagnosis through high sensitivity and specificity, and low false negative rate and false positive rate of lung cancer, could acquire the fusion image of specialized soft tissue by combining the radio-pharmaceuticals with various sequences, so its clinical value and usefulness are regarded as latently sufficient.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 추계학술대회
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• pp.42-47
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• 1990

In this paper, a new adaptive image processing method based on human visual system has been presented. The basic idea behind the proposed method is to improve the efficiency of the information transfer channel regionally by manipulating the displayed image in order to compensate the regional inefficiency of the information transfer channel. The proposed method consists of two parts; the first part reallocates pixel values corresponding to high X-ray attenuation to that of more intense X-ray exposure by multiplying the pixel values with the local adaptive multiplcation factor, and the second part adjusts the pixel values of dark area of displayed image such as overexposed lung area to be more bright. The processed image with the proposed method shows significantly increased visibility of mediastinal and subdiaphramatic area, and also the lung area of over exposed case without any artifact.

• 정보과학회 논문지
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• 제43권3호
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• pp.353-361
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• 2016

폐암 환자의 영상유도 방사선 치료의 경우 환자의 호흡 및 심장박동에 따라 종양의 움직임이 변화할 수 있으므로 치료 시 종양의 움직임을 추적하는 것이 필요하다. 본 논문에서는 치료계획용 4D MDCT 영상과 치료 시 획득한 4D CBCT 영상의 3차원 영상 정보를 기반으로 종양 움직임을 추적하는 방법을 제안한다. 첫째, 효율적으로 치료 시 종양의 움직임을 추적하기 위해 치료계획용 4D MDCT 영상에서 획득한 종양 움직임 모델을 통해 종양의 전역적 움직임을 예측한다. 둘째, 종양 움직임 추적의 정확성을 높이기 위해 4D CBCT 영상에서 종양 주변의 구조적 정보를 이용해 세부적 움직임을 보정하여 종양의 지역적 움직임을 예측한다. 제안방법의 성능 평가를 위해 디지털 팬텀을 이용해 실험한 결과, 지역적 움직임을 고려했을 때 전역적 움직임만 보정한 경우보다 종양 위치화 오류가 45% 감소하였다.

• 한국통신학회논문지
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• 제37권7C호
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• pp.610-618
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• 2012

본 논문에서는 영상 처리 기술을 이용한 색상 분석 프로그램을 개발, 적용하여 흡연이 폐 기능에 미치는 영향을 분석하였다. 즉, 한의학의 진단 이론인 망진을 기반으로 폐와 우측 뺨 영역이 연관되어 있으며 폐 기능이 약해지면 백색에 가까워진다는 내용을 IT기술인 영상 처리 기법을 적용하여 흡연에 따른 얼굴 색상의 변화를 비교, 분석하는 연구를 수행하였다. 이를 위해 20대 남성 피실험자 15명을 대상으로 흡연 전과 후의 우측 뺨 영역의 색상을 추출, 분석하였으며 Lab 색체계에서 a값과 b값이 흡연에 의해 어떻게 변하는지를 분석하고자 하며 이를 기반으로 개인별 편차가 반영된 개인맞춤형 건강관리 시스템을 구축하고자 한다. 실험 결과 흡연에 따라 우측 뺨 영역의 색상이 흡연량에 비례하여 백색에 가까워졌으며 이는 흡연이 폐 기능에 악영향을 미치는 것으로 분석할 수 있었다.

• 대한의용생체공학회:의공학회지
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• 제38권5호
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• pp.237-241
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• 2017

Early detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권1호
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• pp.41-49
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• 2005

본 논문에서는 동일 환자에 대하여 시간차론 두고 촬영한 복부 CT 영상에서 환자의 움직임에 따른 두 영상 간 차이를 보정하기 위하여 지역적 거리전파를 이용한 자동 폐 정합 방법을 제안한다. 본 제안방법은 다음과 같은 세 단계로 구성된다 첫 번째, 일련의 두 볼륨데이타에서 폐 경계를 추출한 후, 폐를 포함하는 최적경계볼륨을 생성하여 초기정합을 수행한다 두 번째, 초기에 촬영한 볼륨데이타에서 지역적 거리전파를 이용하여 폐 경계로부터 3차원 거리맵을 생성한다. 세 번째, 선택적 거리 측정을 통해 두 경계간에 거리차이가 최소인 위치로 영상을 정합한다. 실험으로 3명의 환자 데이타에 대하여 영상정합을 하였고, 기존의 챔퍼매칭 정합 방법과 수행속도와 견고성 측면에서 비교 평가하였다. 본 제안방법은 지역적 거리전파를 사용하여 생성된 3차원 거리맵을 이용한 선택적 거리측정을 통하여 최적의 위치로 빠르고 견고하게 정합된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2352-2355
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• 2003

In recent yeas, studies on computer aided diagnosis (CAD) using image analysis on CT images have been conducted with respect to various diseases. Extracting ground-glass opacities (GGO) on lung CT images is one of such subjects, though it has not found an established method yet. If the region of ground-glass opacities is large on CT images, it can be detected without much difficulty. On the other hand, if the region is small, it is still difficult to find it exactly. In the latter case, increasing overlooking possibility cannot be avoided according to smaller size of the region. To solve this difficulty, this paper proposes an automatic technique for extracting ground-glass opacities on lung CT images employing some statistical parameters of a gray level histogram and a differential histogram. The proposed technique is applied to some lung CT images in the performed experiment. The results are shown with discussion on future work.