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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...
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...
Computer-aided detection (CAD) is increasingly used in clinical practice and for many applications a multitude of CAD systems have been developed. In practice, CAD systems have different strengths and weaknesses and it is therefore interesting to consider their combination. In this paper, we present generic methods to combine multiple CAD systems and investigate what kind of performance increase can...
A pulmonary nodule is the most common sign of lung cancer. The proposed system efficiently predicts lung tumor from Computed Tomography (CT) images through image processing techniques coupled with neural network classification as either benign or malignant. The lung CT image is denoised using non-linear total variation algorithm to remove random noise prevalent in CT images. Optimal thresholding is...
Two-dimensional Principal component analysis (2DPCA) is widely used in face feature extraction and recognition as its lower-computational complexity comparing with principal component analysis (PCA). In this paper, we propose a feature extraction algorithm of pulmonary nodules based on 2DPCA with adaptive parameters. The cumulative variance proportion which is the histogram peak value of CT image...
With the continual improvement in spatial resolution of Nuclear Medicine (NM) scanners, it has become increasingly important to accurately compensate for patient motion during acquisition. Respiratory motion produced by lung ventilation is a major source of artefacts in NM that can affect large parts of the abdominal-thoracic cavity. As such, a particle filter (PF) is proposed as a powerful method...
The analysis of multiple chest radiographs is prone to errors mainly due to different acquisition settings used in clinical routine and due to patient specific characteristics affecting the distribution of the image intensities. The purpose of this paper is twofold: (a) it proposes a new image normalization technique based on samples extracted from the spinal cord/mediastinum region, in order to reduce...
There is a need for a method of tracking lung tumors in beam's-eye-view MV image sequences without implanted radiopaque fiducials. We present a multi-region tracking algorithm to follow lung tumors on CT projections and in-treatment portal image movies before and during external beam radiotherapy, respectively. Finding suitable landmarks for tracking is challenging due to low contrast in the images...
Respiratory gated radiotherapy for lung cancer allows for more precise delivery of prescribed radiation dose to the tumor, while minimizing normal tissue complications. Techniques for fluoroscopic gating without implanted fiducial markers have been developed in a classification framework. Due to the high-dimensionality nature of the images, dimensionality reduction techniques such as principal component...
Lung nodules can be detected through examining CT scans. An automated lung nodule classification system is presented in this paper. The system employs random forests as its base classifier. A unique architecture for classification-aided-by-clustering is presented. Four experiments are conducted to study the performance of the developed system. 5721 CT lung image slices from the LIDC database are employed...
In this paper, we proposed an automatic lung segmentation method. We designed a ROI based method to estimate a proper initial lung boundary for ASM deformation by deriving the translation and the scaling parameters from the lung ROI. An adaptive ASM, using k-means clustering and silhouette-based cluster validation technique, was proposed to adapt to the lung shape change so that the lung shape variation...
We propose a novel approach for on-line treatment verification using cine EPID (electronic portal imaging device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated lung radiotherapy has high precision requirement, and it is essential to effectively monitor the target making sure the tumor is within beam aperture. We model the treatment verification...
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