• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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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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• 제7권2호
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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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• 제14권3호
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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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• 제32권3호
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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.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제7권5호
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• pp.451-457
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• 2001

본 논문은 컴퓨터단층촬영 영상에서의 자동화된 결절성 폐암의 추출 및 체적계산을 수행하였다. 배경으로부터의 흉부 분리 및 흉부로부터 폐영역으로의 분리를 위해 명암값 임계치 방법이 사용되었고, 폐 경계선 주위에 위치하는 폐암을 폐영역으로 포함시키기 위해 스캔닝-볼(Scanning-Ball) 알고리즘을 사용하였다. 폐영역으로부터 높은 명암값을 가진 부분만을 추출하는데, 이는 폐암, 혈관 또는 부분볼륨중의 하나이다. 추출된 폐암 후보자중에서 폐암을 구별하기 위해 크기, 솔리드(Solid) 형태, 평균값, 표준편차, 픽셀의 빈돗수와 명암값과의 상관계수가 사용되었으며, 식별된 폐암의 체적 및 원형율을 계산하였으며, 이를 내림차순으로 분류하였다. 19개 케이스, 총 621개 영상에 적용한 결과, 95%의 폐암추출 민감도를 가진다.

• Tuberculosis and Respiratory Diseases
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• 제56권2호
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• pp.151-158
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• 2004

연구배경 : 임상적 병기 $T_{1-2}N_0M_0$인 비소세포폐암에서 주 종괴와 같은 엽 혹은 다른 엽에 폐 결절이 존재하는 경우, 이 결절의 악성여부에 따라 환자의 예후는 물론 치료방침이 크게 달라진다. 그러나 조직검사가 어려워 악성여부를 감별하기 어려운 경우가 많고, 악성 혹은 양성을 시사하는 임상적, 조직학적 예측인자가 알려져 있지 않아 악성의 빈도 및 예측인자를 알아보고자 본 연구를 시행하였다. 방 법 : 2001년 7월부터 2003년 9월까지 서울대학교병원에서 비소세포폐암으로 수술 받은 환자 444명의 흉부 전산화 단층촬영을 후향적으로 검토하였다. 수술 전 임상적 병기가 $T_{1-2}N_0M_0$이고 주 종괴 외의 폐 결절이 존재하는 환자 중, 결절에 대한 조직검사가 이루어진 경우나 수술 후 최소한 6개월 이상의 추적검사가 가능했던 경우만을 포함하였으며 수술 전후 항암치료나 방사선치료를 시행 받은 경우나 석회화된 결절의 경우는 제외하였다. 결 과 : 대상환자는 총 39명이었으며 이 중 양성이 33 예, 악성이 6 예였다. 양성군과 악성군에 대하여 환자의 성별, 나이, 조직형, 병기, 결절의 모양, 크기, 동반결절유무, 위치, 주변의 석회화 존재여부 등을 비교하였으나 이 중 어느 인자에 관하여도 통계적으로 의미 있는 차이는 관찰되지 않았다. 결 론 : 비소세포암의 임상적 병기가 $T_{1-2}N_0M_0$인 경우, 동반된 폐결절 중 다수가 양성결절이므로 수술적 치료를 고려할 수 있다. 또한 양성 혹은 악성 여부를 시사하는 임상적, 방사선학적 소견이 없으므로 조직학적 확인을 하기 위한 노력이 필요하다.

• 대한임베디드공학회논문지
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• 제17권5호
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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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• 제56권1호
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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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• 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.