• 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.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.335-338
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• 2002

This paper proposes some technical approaches for automatic detection of pulmonary nodules in chest X-ray images. We applied threshold technique for the lung field segmentation and extended the lung field by using morphological methods. A template matching technique was employed for automatic detecting nodules in lung area. Genetic algorithm(GA) was used in template matching(TM) to select a matched image from various reference patterns(simulated typical nodules). We eliminated the false-positive candidates by using histograms and contrasts. We used standard databases published by Japanese Society of Radiological Technology (JSRT) for correct results. Also we employ two-dimensional Gaussian distribution for some reference images because the shadow of lung nodules in radiogram generally shows the distributions. Nodules of about 89% were correctly detected by our scheme. The simulation results show that it is an effective method to indicate lesions on chest radiograms.

• Journal of Yeungnam Medical Science
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• v.7 no.2
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• pp.141-149
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• 1990

The authors conducted a clinical observation of 55 cases of solitary pulmonary nodules at Yeungnam University Hospital from June 1986 to October 1990, and the following results were obtained : 1. The age distribution was ranged from 18 to 77 years, and the male-to-female ratio was 1.8:1. 2. Among 55 cases of nodules, 28 cases were benign and 27 cases were malignant nodules, and of malignant nodules, the primary lung cancer was 23 cases and of benign nodules, 18 cases were tuberculoma. 3. 23 cases (41.8%) was asymptomatic and the other 32 cases were symptomatic; chest pain 12 cases, hemoptysis; 8 cases, cough; 8 cases and dyspnea; 4 cases. 4. The non-smoker-to-smoker ratio was 1:1.04, but among 23 smoker over 20 pack years, 14 cases were malignant nodules. 5. According to nodular size, there is no striking differences between benign and malignant nodules except 3-4cm sized nodules. 6. The lobar distribution of nodules, 35 cases were in the right lung (upper lobe; 14 cases, middle lobe; 11 cases, and lower lobe; 10 cases) and 20 cases were in the left lung(upper lobe; 9 cases, lower lobe; 11 cases), and the malignant nodules were most commonly observed in the right upper lung.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1104-1120
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• 2020

The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods.

• Radiation Oncology Journal
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• v.32 no.3
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• pp.116-124
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• 2014

Purpose: To investigate the frequency and clinical significance of detected incidental lung nodules found on computed tomography (CT) simulation images for hepatocellular carcinoma (HCC) using computer-aided diagnosis (CAD) and a physician review. Materials and Methods: Sixty-seven treatment-$na{\ddot{i}}ve$ HCC patients treated with transcatheter arterial chemoembolization and radiotherapy (RT) were included for the study. Portal phase of simulation CT images was used for CAD analysis and a physician review for lung nodule detection. For automated nodule detection, a commercially available CAD system was used. To assess the performance of lung nodule detection for lung metastasis, the sensitivity, negative predictive value (NPV), and positive predictive value (PPV) were calculated. Results: Forty-six patients had incidental nodules detected by CAD with a total of 109 nodules. Only 20 (18.3%) nodules were considered to be significant nodules by a physician review. The number of significant nodules detected by both of CAD or a physician review was 24 in 9 patients. Lung metastases developed in 11 of 46 patients who had any type of nodule. The sensitivities were 58.3% and 100% based on patient number and on the number of nodules, respectively. The NPVs were 91.4% and 100%, respectively. And the PPVs were 77.8% and 91.7%, respectively. Conclusion: Incidental detection of metastatic nodules was not an uncommon event. From our study, CAD could be applied to CT simulation images allowing for an increase in detection of metastatic nodules.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.451-457
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• 2001

This paper describes automated methods for the detection of lung nodules and their volume calculation on CT scans. Gray-level threshold methods were used to segment the thorax from the background and then the lung parenchymes from the thoracic wall and mediastinum. A scanning-ball algorithm was applied to more accurately delineate the lung boundaries, thereby incorporating peripheral nodules contiguous to pleural surface within the segmented lung parenchymes. The lesions which have the high gray value were extracted from the segmented lung parenchymes. The selected lesions include nodules, blood vessels and partial volume effects. The discriminating features such as size, solid-shape, average, standard deviation and correlation coefficient of selected lesions were used to distinguish true nodules from pseudo-lesions. Volume and circularity calculation were performed for each identified nodules. The identified nodules were sorted in descending order of the volume. These method were applied to 621 image slices of 19 cases. The sensitivity was 95% and there was no false-positive result.

• Tuberculosis and Respiratory Diseases
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• v.56 no.2
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• pp.151-158
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• 2004

Background : When a non small cell lung caner patient at the $_cT_{1-2}N_0M_0$ stage is diagnosed with intrapulmonary nodule(s), the treatment plan and prognosis of the patient largely depend on whether the nodule is benign or malignant. In most cases, however, it is hard to conduct a biopsy on such a nodule, due to its small size. Furthermore, the predictive factors that may imply benignancy or malignancy of the nodules remain unknown. As such, the purpose of our study was to validate the incidence of malignant nodules in such cases, and find if there are any predictive factors. Methods : Chest computed tomography(CT) scans and the medical records of 444 patients, who had undergone non small cell lung cancer surgery, between July, 2001 and September, 2003, at Seoul National University Hospital, were retrospectively reviewed. Among $_cT_{1-2}N_0M_0$ non small cell lung cancer patients, with intrapulmonary nodule(s), only those cases where a CT scan or a biopsy of the nodules had been conducted, and had been followed up at intervals of more than 6 months were included. However, patients who had received chemotherapy or radiation therapy, pre- or post-operatively, or with calcified nodules, were excluded. Results : Our study group consisted of 39 patients, divided into two groups. The first group, 33 patients, had benign nodules, and the second group, 6 patients, had malignant nodules. The two groups were compared with regard to gender, age, cell type, pathologic stage, shape, size, location and number of nodules and presence of calcification around the nodules. There was no statistically significant difference between the two groups. Conclusion : The intrapulmonary nodules in non small cell lung cancer patients at the $_cT_{1-2}N_0M_0$ stage were mostly benign. Therefore, surgical treatment for such patients can be considered. Moreover, without predictive factors, pathological confirmation of the diagnosed nodules should be sought in all patients.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.265-272
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• 2022

As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.

• Journal of Chest Surgery
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• v.56 no.1
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• pp.1-5
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• 2023

Background: The number of patients with incidentally identified pulmonary nodules is increasing. This study attempted to confirm the usefulness and safety of video-assisted thoracic surgery (VATS) core needle biopsy of pulmonary nodules. Methods: Data from 18 patients diagnosed with pulmonary nodules who underwent VATS core need biopsy were retrospectively reviewed. Results: Of the 18 patients, 15 had malignancies (primary lung cancer, n=14; metastatic lung cancer, n=1), and 3 had benign nodules. Mortality and pleural metastasis did not occur during the follow-up period. Conclusion: In patients with solitary pulmonary nodules that require tissue confirmation, computed tomography-guided percutaneous cutting needle biopsy or diagnostic pulmonary resection sometimes may not be feasible choices due to the location of the solitary pulmonary nodule or the patient's impaired pulmonary function, VATS core needle biopsy may be performed in these patients as an alternative method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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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.