• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.347-370
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• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.705-713
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• 2024

This article discusses studies and real-world experiences related to the clinical application of artificial intelligence-based computer-aided detection (AI-CAD) software (LuCAS-plus, Monitor Corporation) in detecting pulmonary nodules. During clinical trials for lung cancer screening, AI-CAD exhibited performance comparable to that of medical professionals in terms of sensitivity and specificity. Studies revealed that applying AI-CAD for diagnosing pulmonary metastases led to high detection rates. The use of a nodule matching algorithm in diagnosing pulmonary metastases significantly reduced false non-metastasis results. In clinical settings, implementing AI-CAD enhanced the efficiency of pulmonary nodule detection, saving time and effort during CT reading. Overall, AI-CAD is expected to offer substantial support for lung cancer screening and the interpretation of chest CT scans for malignant tumor surveillance.

• Korean Journal of Radiology
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• v.23 no.1
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• pp.139-149
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• 2022

Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). Materials and Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. Results: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. Conclusion: BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

• 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.41 no.4
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• pp.435-439
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• 1994

Lung cancer is the most common fatal malignant lesion in both sexes. Detection of the solitary pulmonary nodule is important because surgical series up to a third of solitary pulmonary nodules are bronchogenic carcinoma. Bronchioloalveolar cell carcinoma is a rare primary lung cancer and surgery is treatment of choice in brochioloalveolar cell carcinoma. We experinced a case of bronchioloalveolar cell carcinoma in solitary pulmonary nodule with cavitary lesion in chest CT scan, which is an uncommon finding in brochioloalveolar cell carcinoma.

• The Korean Journal of Nuclear Medicine
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• v.35 no.6
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• pp.398-400
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• 2001

The authors report a case of unsuspected myocardial ischemia detected during CoDe FDG PET (coincidence detection fluorodeoxyglucose positron emission tomogram) which was performed for the evaluation of a solitary pulmonary nodule. Camera-based FDG PET without attenuation correction often reveals false defect in the inferior wall of the left ventricle in normals due to excessive attenuation. However, this asymptomatic patient had increased uptake in the inferior wall suggesting ischemic myocardium. The scan finding was confirmed by Tl-201 myocardial SPECT and coronary angiogram. The patient then underwent successful PTCA of mild RCA and right ventricular branch followed by right upper lobectomy for small cell lung cancer.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.1
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• pp.13-19
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• 2012

In this paper, we propose the pulmonary nodule detection method based on hierarchical 3D block analysis. The proposed system consists of two main part. In the first part, we select the block which is need to analysis. In the second part, we analysis the selected blocks. We extract the shape based features of the object in the selected blocks. Support Vector Machine is applied to the extracted features to classify into nodules and non-nodules.

• Journal of Biomedical Engineering Research
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• v.11 no.2
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• pp.209-216
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• 1990

Pulmonary nodules in chest X-Ray images were detected using the symmetric property of human lung and its performance was evaluated. Thls algorithm reduced the effect of background components and enhanced the nodule signals relatively. The image was divided and processed separately, the half with matched filter only, and the other half with warping and matched filter. This algorithm increased the entire detection rate by reducing False-Positive error and improving True-Positive detectability. Result shows 10-25 % improvement in detection rate compared with the conventional alsorithm for nodules size of 10mm.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.729-737
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• 2020

Screening with low-dose spiral computed tomography (LDCT) has been shown to reduce lung cancer mortality by about 20% when compared to standard chest radiography. One of the problems arising from screening programs is that large amounts of CT image data must be interpreted by radiologists. To solve this problem, automated detection of pulmonary nodules is necessary; however, this is a challenging task because of the high number of false positive results. Here we demonstrate detection of pulmonary nodules using six off-the-shelf convolutional neural network (CNN) models after modification of the input/output layers and end-to-end training based on publicly databases for comparative evaluation. We used the well-known CNN models, LeNet-5, VGG-16, GoogLeNet Inception V3, ResNet-152, DensNet-201, and NASNet. Most of the CNN models provided superior results to those of obtained using customized CNN models. It is more desirable to modify the proven off-the-shelf network model than to customize the network model to detect the pulmonary nodules.