• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.1
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• pp.37-44
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• 2013

In this paper, we propose the pulmonary nodule detection system based on voxel classification. The proposed system consists of three main steps. In the first step, we segment lung volume. In the second step, the lung structures are initially segmented. In the last step, we classify the nodules using voxel classification. To describe characteristics of each voxel, we extract the log-polar sampling based features. Support Vector Machine is applied to the extracted features to classify into nodules and non-nodules.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.35-43
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• 2009

In the Computer Aided Diagnosis(CAD) System, the efficient way of classifying nodules from chest CT images of a patient is to perform the classification of the remaining part after the pulmonary vessel extraction. During the pulmonary vessel extraction, due to the small difference between the vessel and nodule features in imaging studies such as CT scans after having an injection of contrast, nodule maybe extracted along with the pulmonary vessel. Therefore, the pulmonary vessel extraction method plays an important role in the nodule classification process. In this paper, we propose a nodule reclassification method based on vessel thickness analysis. The proposed method consist of four steps, lung region searching step, vessel extraction and thinning step, vessel topology formation and correction step and the reclassification of nodule in the vessel candidate step. The radiologists helped us to compare the accuracy of the CAD system using the proposed method and the accuracy of general one. Experimental results show that the proposed method can extract pulmonary vessels and reclassify false-positive nodules accurately.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.994-1003
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• 2017

Ground-glass opacity nodules(GGNs) in chest CT images are associated with lung cancer, and have a different malignant rate depending on existence of solid component in the nodules. In this paper, we propose a method to classify pure GGNs and part-solid GGNs using multiview images and texture analysis in pulmonary GGNs with solid components of 5mm or smaller. We extracted 1521 features from the GGNs segmented from the chest CT images and classified the GGNs using a SVM classification model with selected features that classify pure GGNs and part-solid GGNs through a feature selection method. Our method showed 85% accuracy using the SVM classifier with the top 10 features selected in the multiview images.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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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.

• Tuberculosis and Respiratory Diseases
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• v.69 no.5
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• pp.354-360
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• 2010

Background: Primary non-Hodgkin's lymphoma of the lung is a rare entity. It is represented commonly as marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue (MALT) type. Although there have been a few reviews of this lymphoma, clinical features, radiologic findings, management and prognosis have not been well defined. Methods: We reviewed the medical records of 24 patients with primary pulmonary lymphoma between January 1995 and September 2008; all diagnoses had been confirmed based on pathology. Results: The median follow-up time was 42.3 months (range, 0.1~131.2 months). Five (20.8%) patients were asymptomatic, 17 (70.8%) patients had pulmonary symptoms, and the remaining 2 (8.3%) patients presented with constitutional symptoms. There were 16 (66.7%) patients with MALT lymphoma, 4 (16.7%) patients with diffuse large B-cell lymphoma and 4 (16.7%) patients with lymphoma that had not received a WHO classification. Radiologic findings of primary pulmonary lymphoma were diverse and multiple nodule or consolidation was the most common finding regardless of pathologic lymphoma type. PET scan was carried out in 13 (54.2%) patients and all lesions showed notable FDG uptake. MALT lymphoma showed a trend of better prognosis (3-year survival, 78.8% vs. 70.0%; 5-year survival, 78.8% vs. 52.5%; p=0.310) than non-MALT lymphoma. Conclusion: Primary non-Hodgkin's lymphoma of the lung occurs with nonspecific clinical features and radiologic findings. MALT lymphoma is the most common pathologic type of primary pulmonary lymphoma. This entity of lymphoma appears to have a good prognosis and in this study, there was a trend of better outcome than non-MALT lymphoma.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.1
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• pp.87-92
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• 2016

In this paper, we improved classification performance of benign and malignant lung nodules by including the parenchyma features. For small pulmonary nodules (4-10mm) nodules, there are a limited number of CT data voxels within the solid tumor, making them difficult to process through traditional CAD(computer aided diagnosis) tools. Increasing feature extraction to include the surrounding parenchyma will increase the CT voxel set for analysis in these very small pulmonary nodule cases and likely improve diagnostic performance while keeping the CAD tool flexible to scanner model and parameters. In AdaBoost learning using naive Bayes and SVM weak classifier, a number of significant features were selected from 304 features. The results from the COPDGene test yielded an accuracy, sensitivity and specificity of 100%. Therefore proposed method can be used for the computer aided diagnosis effectively.

• Journal of Preventive Medicine and Public Health
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• v.29 no.3 s.54
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• pp.471-482
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• 1996

Early recognition of coalescence in pneumoconiotic lesions is important because such coalescence is associated with the respiratory symptoms and deterioration of lung function. This complicated form of pneumoconiosis also has worse prognosis than does simple pneumoconiosis. High resolution computerized tomography(HRCT) provides significant additional information on the stage of the pneumoconiosis because it easily detects coalescence of nodules and emphysema that may not be apparent on the simple radiograph. The purpose of this study is to clarify the role of HRCT in detection of large opacity and the relationship of change between the coalescence of nodules or emphysema and lung function in dust exposed workers. 1. There was good correlation between the HRCT grade of pneumoconiosis and ILO category of profusion. 5(9.09%) in 55 study population had confluent nodule extending eve, two o, more cuts on HRCT. HRCT could identify the pneumoconiotic nodules which was not found by simple radiogrphy in 6 workers with category 0/0. 2. No significant difference was observed coalescence of nodules and emphysema by dust type. 3. There was no significant difference in pulmonary function according to ILO and HRCT classification. 4. HRCT could detect the significant reduction in $FEV_1,\;FEV_1/FVC$, PEFR, $FEF_{25},\;FEF_{50},\;and\;FEF_{75}$ and remarkable increase in RV and TLC in study persons with emphysema compared with non-emphysema group. 5. Emphysema was found more often in nodules-coalescence group than small opacity group by HRCT. We found that HRCT could easily detect areas of coalescence and complicated emphysema compared to plain chest X-ray. Also our data suggest that it is primarily the degree of emphysema rather than the degree of pneumoconiosis that determines the level of pulmonary function.