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Detection of implanted iodine-125 seeds in postoperative CT is a necessary step for evaluating the output of seed implantation brachytherapy of lung tumor. In this paper, we propose a semi-automated method to detect implanted seeds in postoperative lung CT. Three main steps are included in our approach. Firstly, the ROI (Region Of Interest) containing all seeds is extracted from the original image...
Lung magnetic resonance imaging (MRI) using hyperpolarized 129Xe as contrast agent is an emerging medical imaging technique for respiratory disease diagnosis and therapy evaluation. 3D model can help doctors to have more insights of patients' lung in disease diagnosis and surgical planning. Conventional 3D reconstruction requires a large number of scanning layers. Different from conventional proton...
For lung tissue adhesion of the situation in the lung CT images, based on the classical watershed algorithm, this introduce line-encoded ideology, use contour tracing method to determine adhesion region segmentation points, according to segment code to get the split line, thus separating the adhesion region. Experimental results show that this method will be effective in the lung adhesion region segmentation,...
In this paper 2D Otsu algorithm based on particle swarm optimization (PSO) is proposed to segment CT lung images. This method can extract pulmonary parenchyma from multisliced CT images, which is primary step to detect the pulmonary disease such as lung cancer, tumor, and mass cells. In the automated pulmonary disease diagnosis, image segmentation plays an important role and image analysis result...
Modern multislice computed tomography (CT) scanners produce isotropic CT images with a thickness of 0.6 mm. These CT images offer detailed information of lung cavities, which could be used for better surgical planning of treating lung cancer. The major challenge for developing a surgical planning system is the automatic segmentation of lung lobes by identifying the lobar fissures. This paper presents...
Tracking of the lung tumor movement in fluoroscopic video sequences is clinically significant and challenging problem due to the blurred appearance, sternum occlusion, and complicate intra- and inter- fractional motion. This introduces landmark ambiguity for accurate contour tracking. As the boundary of the lung, or part of the lung, is usually clear and can be accurately tracked, we propose a novel...
Positron emission tomography - computed tomography (PETCT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PET-CT images, however, is not a trivial task. We propose a content-based image retrieval system for retrieving similar cases from an imaging database as a reference dataset to aid the physicians in PET-CT scan interpretation...
Pleural effusions are accumulations of fluid in the pleural space, usually associated with atelectasis of the adjacent lung. We have previously presented an automated method to measure the volume of pleural effusions on chest CT images. This paper presents an improved version of the same method, which adds 3D surface modeling and additional propagation of the segmentation in the inferior direction...
Traditional deformable image registration imposes a uniform smoothness constraint on the deformation field. This is not appropriate when registering images visualizing organs that slide relative to each other, and therefore leads to registration inaccuracies. In this paper, we present a deformation field regularization term that is based on anisotropic diffusion and accommodates the deformation field...
Lung function depends on mechanical lung expansion and contraction during the respiratory cycle. Recently developed dynamic 4D CT imaging and 3D image registration can be used to analyze regional lung function, which are significant for lung disease diagnosis, treatment and lung ventilation change during radiation therapy. 4D CT images of the lung can be reconstructed at any respiratory phase point...
An alternative method of diagnosing malignant lung nodules by their shape rather than conventional growth rate is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis that represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape...
This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
We present a simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs. The user provides a partial segmentation of the organ of interest, which is used to guide the template towards its target. The user also highlights some elements of the background that should be excluded from the final segmentation. We derive by likelihood maximization a registration...
In this paper we propose a new scheme for measuring regional ventilation from tagged hyperpolarized helium-3 MR images. A new registration cost function that incorporates both the intensity information (SSD) and the shape feature (SSBMD) from the images is proposed for registering end inspiration to the end expiration image. The smoothness of the displacement field is maintained by incorporating the...
This work presents an integrated framework for elastic image registration with log-unbiased deformations and a spatially variable constraint to reduce image folding and preserve the rigidity of bony structures. The framework has been applied to the data provided by the workshop on Evaluation of Methods for Pulmonary Image Registration 2010 (EMPIRE10). We have compared our new method to the classic...
Lung segmentation is an important first step for quantitative lung CT image analysis and computer aided diagnosis. However, accurate and automated lung CT image segmentation may be made difficult by the presence of the abnormalities. Since many lung diseases change tissue density, resulting in intensity changes in the CT image data, intensity-only segmentation algorithms will not work for most pathological...
We propose in this paper automatic algorithm for early planocellular lung cancer detection in lung X ray images. Considering the fact that lung cancer is one of the most lethal cancers and that it is usually diagnosed too late, the solution is to attempt early diagnosis at general practitioners level, using cheapest diagnostic tools, chest radiography. The proposed algorithm determines and segments...
The early detection of the lung cancer is a challenging problem, due to the structure of the cancer cells. This paper presents two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The manual analysis of the sputum samples is time consuming, inaccurate and requires intensive...
Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful...
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