• Journal of Korea Multimedia Society
• /
• v.16 no.6
• /
• pp.700-707
• /
• 2013

In this paper, we propose a non-rigid registration method of lung parenchyma in temporal chest CT scans using region binarization modeling and locally deformable model. To cope with intensity differences between CT scans, we segment the lung vessel and parenchyma in each scan and perform binarization modeling. Then, we match them without referring any intensity information. We globally align two lung surfaces. Then, locally deformable transformation model is developed for the subsequent non-rigid registration. Subtracted quantification results after non-rigid registration are visualized by pre-defined color map. Experimental results showed that proposed registration method correctly aligned lung parenchyma in the full inspiration and expiration CT images for ten patients. Our non-rigid lung registration method may be useful for the assessment of various lung diseases by providing intuitive color-coded information of quantification results about lung parenchyma.

• Radiation Oncology Journal
• /
• v.15 no.4
• /
• pp.387-392
• /
• 1997

Purpose : To quantify the movement of lung Parenchyma for ICRU 50 Planning Target Volume (PTV) delineation of the lung region. Materials and Method : Fluoroscopic observations and measurements are Performed on 10 patients with chest region cancer who have normal putmonary functions We have divided the lung region into 12 parts for the right lung, 10 parts for the left lung and four to five Points of lung parenchyma were selected for anatomical analysis Points, Fluoroscopic images are sent to a computer and then movements are measured. Results : Both lowe lobes showed the longest longitudinal movements because of breathing (average 14.1mm, maximum 22.1mm), while anteroposterior displacement showed the smallest value. Lateral movements of the lung parenchyma averaged 6.6mm, and the maximum value was 9.1mm, (both hilar regions showed maximum values because of cardiac motion) Conclusion : We could quantify the lung movements by measuring parenchyma displacements. The movements of both upper lobes were less than those of the middle and upper lobes in longitudinal and transverse movements. Optimal margins can be selected for PTV delineation using these results.

• Journal of Sensor Science and Technology
• /
• v.8 no.2
• /
• pp.154-162
• /
• 1999

In this paper, we acquired EBT section images of lung parenchyma using fabricated spirometric gating device and proposed new energy function based on dynamic contour model in order to extracted the contour of the lung parenchyma in EBT images. In EBT images, gray level of the lungs is lower than other region. we extracted the lungs contour using the new energy function considering gray level and contour vector of the lung parenchyma region from EBT images. As we compared the proposed method with the conventional method, we confirmed that detection method using proposed energy function was valid.

• Journal of Korea Multimedia Society
• /
• v.8 no.5
• /
• pp.641-650
• /
• 2005

In this parer, curve stopping function based on the CT number of lung parenchyma from CT lung images is proposed to detect lung region in replacement of conventional edge indication function in geodesic active contour model. We showed that the proposed method was able to detect lung region more effectively than conventional method by applying three kinds of measurement numerically. And, we verified the effectiveness of proposed method visually by observing the detection Procedure on actual CT images. Because lung parenchyma region could be precisely detected from actual EBCT (electron beam computer tomography) lung images, we were sure that the Proposed method could aid to early diagnosis of lung disease and local abnormality of function.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.10
• /
• pp.2163-2170
• /
• 2009

We present an automated, energy minimized-based method for Lung parenchyma segmenting Chest Computed Tomography(CT) datasets. Deformable model is used for energy minimized segmentation. Quantitative knowledge including expected volume, shape of Chest CT provides more feature constrain to diagnosis or surgery operation planning. Segmentation subdivides an lung image into its consistent regions or objects. Depends on energy-minimizing, the level detail image of subdivision is carried. Segmentation should stop when the objects or region of interest in an application have been detected. The deformable model that has attracted the most attention to date is popularly known as snakes. Snakes or deformable contour models represent a special case of the general multidimensional deformable model theory. This is used extensively in computer vision and image processing applications, particularly to locate object boundaries, in the mean time a new type of external force for deformable models, called gradient vector flow(GVF) was introduced by Xu. Our proposed algorithm of deformable model is new external energy of GVF for exact segmentation. In this paper, Clinical material for experiments shows better results of proposal algorithm in Lung parenchyma segmentation on Chest CT.

• Journal of Veterinary Clinics
• /
• v.20 no.4
• /
• pp.493-495
• /
• 2003

A dead, female, 3 years old, squirrel monkey (Saimiri sciureus) was submitted and examined. Before death the monkey has showed lethargy, recumbency and inappetence since November 14, 2001 and died in November 17. Grossly much fibrin was deposited on the pleura of right lung, pericardium, and diaphragm(pleural part). And reddening of right lung was seen. Histopathologically lung showed severe fibrinous pleuritis, severe edema, thrombosis, and focal necrosis in parenchyma. Also much fibrin and mononuclear cells were deposited on the pericardium. In bacterial culture on the pleura and parenchyma of lung, and pericardium, B. bronchiseptica was isolated. Therefore we confirmed this case as the fatal case by B. bronchiseptica in squirrel monkey.

• Korean Journal of Radiology
• /
• v.22 no.3
• /
• pp.476-488
• /
• 2021

Objective: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images. Materials and Methods: Thin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31-89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation. Results: The Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model). The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model). Conclusion: The deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.

• Journal of Chest Surgery
• /
• v.18 no.3
• /
• pp.474-481
• /
• 1985

Pulmonary cavity is the result of necrosis of lung parenchyma with evacuation of the necrotic material via the tracheobronchial tree. A communication with the tracheobronchial tree permits air to enter the area of necrosis, so the radiologic result show the a lucent defect. The radiologic characteristics of the wall of a cavity are determined by the reaction of the lung parenchyma to the pathologic process. Therefore, the shadows of the chest films in cavitary lesion were variable in its nature. The author, in 42 cases which have a cavitary lesion in X-ray findings among 172 cases resected lung obtained in P.N.U.H. from 1979 to June, 1985, studied similarities and differences between the pathogenesis of these lesions and the radiologic findings. The author reviewed the 42 cavitary lesions and the following results were obtained. 1. The cavitary lesions were seen in 42 [24.4%] out of 172 cases of resected lung disease. 2. Histopathologically, pulmonary tuberculosis was 47.6% and primary lung cancer was 9.5%. 3. The most common site of the lesion was right upper lobe. 4. The most common size of the cavity was from 3 to 6 cm in diameter. 5. Lobectomy was the most common operated method.

• Journal of Korea Multimedia Society
• /
• v.8 no.3
• /
• pp.336-344
• /
• 2005

In this paper, we extracted the contour of lung parenchyma on EBT images with the improved active contour model. The objects boundary in conventional active contour model can be extracted by controlling internal energy and external energy as energy minimizing form. However, there are a number of problems such as initialization and the poor convergence about concave part. Expecially, contour can not enter the concave region by discouraging characteristic about stretching and bending in internal energy. We controlled internal energy by moving local perpendicular bisector point of each control point in the contour and implemented the object boundary by minimizing energy with external energy The convergence of concave part could be efficiently implemented toward lung parenchyma region by this internal energy and both lung images for initial contour could also be detected by multi-detection method. We were sure this method could be applied detection of lung parenchyma region in medical image.

• Journal of Veterinary Clinics
• /
• v.26 no.1
• /
• pp.72-75
• /
• 2009

Pulmonary bullae are air-filled spaces within the lung parenchyma that result from the destruction, dilatation and confluence of adjacent alveoli. Pulmonary bullae are found most often in healthy, middle aged, large breed or deep-chested dogs that have no previous history of lung disease and bulla may occur as a result of emphysema, inflammation or trauma. Clinical signs include respiratory distress, anorexia, depression and tachypnea. In this study, a dog with respiratory distress by traffic accident was diagnosed as pulmonary bullae with pneumothorax using radiography at Veterinary Medical Teaching Hospital, GNU. In radiographs, various sized, smooth margin, well defined, oval shaped, gas or fluid filled multiple bullae are shown in the left cranial, left caudal and right accessory lobes. At the initial stage of observation, there were indications of four bullae, two of which were not found in the following radiograph. At the same time, there were serious indications of lung consolidation that caused respiratory distress of patient. Ultimately, the patient was expired after ten hours.