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This paper describes a method for medical images annotation based on the SURF descriptor and the SVM classifier. For the features extraction a Fast-Hessian detector was used. The feature matching was performed with a SVM with a quadratic kernel. The testing of the developed system was performed using a subset of the IRMA radiographic images. The results provided with the SURF descriptor are compared...
We present an automatic method to track individual nodule progression in a lung cancer mouse model. Fourteen A/J mice received an intraperitoneal injection of urethane. Respiratory-gated micro-CT images of the lungs were taken 8, 22, and 37 weeks after injection, at which 195, 585 and 636 nodules were manually detected. The three images from every animal were registered and their nodules matched with...
This article investigates the suitability of local intensity distributions to analyze six emphysema classes in 342 CT scans obtained from 16 sites hosting scanners by 3 vendors and a total of 9 specific models in subjects with Chronic Obstructive Pulmonary Disease (COPD). We propose using kernel density estimation to deal with the inherent sparsity of local intensity histograms obtained from scarcely...
PET-CT is now accepted as the best imaging technique for non-invasive staging of lung cancers, and a computer-based abnormality detection is potentially useful to assist the reading physicians in diagnosis. In this paper, we present a new fully-automatic approach to detect abnormalities in the thorax based on global context inference. A max-margin learning-based method is designed to infer the global...
More and more researchers are beginning to use multiple dissimilarity metrics or image features for medical image registration. In most of these approaches, however, weights for ranking the relative importance between the selected metrics are empirically tuned and fixed for the entire image domain. Different parts of a medical image, however, may contain significantly different appearance properties...
In this paper, we investigate the use of a general motion model for motion compensation of CT/X-ray fusion during image-guided bronchoscopy, where respiratory motion causes a misalignment between the pre-operative CT and the intra-operative X-ray images. The general motion model is learned from four-dimensional images of different patients and captures statistics of lung motion. For motion compensation,...
Shape information plays an important role in biomedical image analysis because of the strong shape characteristics of biological structures. It is often used as a prior to constrain or refine the intermediate shape information derived from low-level image features. In this paper, we give an overview of the sparse shape composition based prior modeling method and its various applications of biomedical...
In this, paper a new approach for lung nodules detection from LDCT scans is proposed. Intensity models of the nodules are generated using an active appearance model formulation. Template matching is used to compute a similarity score between the AAM template and the input image. The goal is to maximize the similarity measure at different image pixels to increase nodule detection. Conventional template...
During radiotherapy of lung tumors, the respiratory motion must be tracked to reduce radiation of healthy tissue. This is usually done by using a respiratory surrogate, but with limited accuracy. We investigate how patient-specific finite element models (FEM) of respiratory mechanics can predict the motion of the lungs. First, the anatomical models of the lungs and thorax are extracted from CT images...
For deformable registration of computed tomography (CT) scans in image guided radiation therapy (IGRT) we apply Riemannian elasticity regularization. We explore the use of spatially varying elasticity parameters to encourage bone rigidity and local tissue volume change only in the gross tumor volume (GTV) and the lungs. We evaluate the method on the point-validated 4DCT breathing thorax POPI-model...
In this paper, we present a new algorithm to register 3D multimodal images with sliding conditions in a diffeomorphic framework. Our driving motivation is to define one-to-one mappings between CT/MR pulmonary volumes acquired from patients with empyema. The main problem to overcome is that the pulmonary motion, which can be large, presents sliding conditions at the thoracic cage boundary. Our algorithm...
The most common cause of acute pulmonary hypertension is pulmonary embolism (PE). Classification of PE severity can be based on obstruction indices that are estimated from clinical imaging, however, as patients with apparently similar levels of obstruction can have quite different clinical outcomes, obstruction indices currently have limited use clinically. Embolus size and location affects patient...
The locations of lung nodules relative to the other lung anatomical structures are important hints of malignant cancers. In this paper, we propose a fully automatic method to identify if a lung nodule is well-circumscribed, juxta-vascular, juxta-pleural or pleural tail in computed tomography (CT) images. First, we design an optimized graph model, introducing new global and region-based energy terms,...
Low attenuation clusters (LACs) are associated with the presence of emphysema in low dose computer tomography (LDCT) scans. It is believed that the LACs' frequency-size distribution can be appropriately fit with power law and be well described with the scale exponent. In this work, we investigated whether the LACs' frequency-size distribution of a group of 80 LDCT scans taken from a lung cancer-screening...
In this paper, we present a fully automatic method to quantify Tree-in-Bud (TIB) patterns for respiratory tract infections. The proposed quantification method is based on our previous effort to detect and track TIB patterns with a computer assisted detection (CAD) system [9]. In addition to accurately identifying TIB on CT, quantifying TIB is important for measuring the volume of affected lung as...
We propose a novel approach for diagnosing malignant lung nodules based on analyzing the spatial distribution of Hounsfield values for the detected lung nodules. Spatial distribution of image intensities (or Hounsfield values) comprising the malignant nodule appearance is accurately modeled with a new rotationally invariant second-order Markov-Gibbs Random Field (MGRF). In this paper, we introduce...
We present an image pipeline for airway phenotype extraction suitable for large-scale genetic and epidemiological studies including genome-wide association studies (GWAS) in Chronic Obstructive Pulmonary Disease (COPD). We use scale-space particles to densely sample intraparenchymal airway locations in a large cohort of high-resolution CT scans. The particle methodology is based on a constrained energy...
Mutual information (MI) has been widely used in image analysis tasks such as feature selection and image registration. In particular, it is the most widely used similarity measure for intensity based registration of multimodal images. However, a major drawback of MI is that it does not take the spatial neighbourhood into account. An effective way of incorporating spatial information could be of great...
The main function of the respiratory system is gas exchange. Since many disease or injury conditions can cause biomechanical or material property changes that can alter lung function, there is great interest in measuring regional lung function and regional mechanical changes. The mechanical properties of lung parenchymal tissue are both elastic and dissipative, as well as being highly nonlinear. A...
Lobe detection from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. We present a fully automated method for segmenting the pulmonary lobes. We first build a lobar approximation by applying a watershed transform to a vesselness density filter, using seed points generated from segmentation and...
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