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Artery-vein classification on pulmonary computed tomography (CT) images is becoming of high interest in the scientific community due to the prevalence of pulmonary vascular disease that affects arteries and veins through different mechanisms. In this work, we present a novel approach to automatically segment and classify vessels from chest CT images. We use a scale-space particle segmentation to isolate...
We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images...
Aortic dissection is a condition in which a tear in the inner wall of the aorta allows blood to flow between two layers of the aortic wall. Aortic dissection is associated with severe chest pain and can be deadly. Contrast-enhanced CT is the main modality for detection of aortic dissection. Aortic dissection is one of the target abnormalities during evaluation of a triple rule-out CT in emergency...
Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this...
Delineation of blurry boundary from medical images is challenging in particular when the target object or region of interest is adjacent to other tissues with similar or overlapping intensity distributions. To address this challenge, we propose a graph model with adaptive global and geodesic constraints to contour the indistinct boundary from CT images. The global factor reflects the appearance affinities...
Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel...
Cone-beam CT (CBCT) is been increasingly used in radiation therapy for patient setup, dose verification and adaptive planning. However, the non-uniformity artifacts on CBCT images mainly caused by x-ray scatter contamination are considered as one of the fundamental limitations that prevents CBCT from widespread clinical practice. In this study, we propose an image-domain non-uniformity correction...
Detection of calcified plaques in coronary arteries is helpful in cardiovascular disease risk assessment. This is often performed by radiologists on computed tomography (CT) images. We work towards an automatic solution for calcium detection in CT images. Most of previous work in this area combines CT and CTA for this purpose to facilitate the localization of the coronary arteries. Given the cost...
Streaking artifacts caused by metallic objects severely affect the visual quality of CT images, resulting in medical misdiagnosis. Commonly used approaches for metal artifact reduction usually consist of interpolation and iterative methods. The former one tends to lose image quality by introducing extra artifacts, while the latter is more computational expensive. This paper proposes a new approach...
Most of abdominal CT images include Gaussian noise, and CT scans form a blurry vision because of the internal fat tissue inside of abdomen. These two handicaps (noise and fat tissue) constitute an impediment in front of an accurate abdominal organ & tumour segmentation. Also segmentation techniques generally fall into error on segmentation of close grayscale regions. Therefore, denoising and enhancement...
In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning...
We propose an automated platform for extra-coronary calcification detection on low dose CT scans. We utilize faster regional convolutional neural networks (R-CNN) to directly detect calcifications at the lesion-level without performing vessel extraction. To segment detected calcifications at the voxel-level, we employ holistically nested edge detection (HED). CT scans of 112 vasculitis patients and...
Positron Emission Tomography (PET) using 18F-FDG is recognized as the modality of choice for lymphoma, due to its high sensitivity and specificity. Its wider use for the detection of lesions, quantification of their metabolic activity and evaluation of response to treatment demands the development of accurate and reproducible quantitative image interpretation tools. An accurate tumour delineation...
The increased utilization of Computer Aided diagnosis (CAD) in clinical procedures has been very effective in discovering numerous abnormalities in human beings. CAD of lung nodules can be safely employed to validate the opinion of radiologists in discovering existence of nodules and assess the existence and severity of lung cancer. This paper provides a comprehensive review of the existing automated...
Liver tumor segmentation is a hot issue in current medical image processing research, fast and accurate liver tumor segmentation method for abdominal CT sequences is the basis for liver lesions diagnose. The evolving curve in traditional level set algorithm often stopped in local gradient minimal regions or false edges while dealing with low contrast or borderline blurred CT images. In order to solve...
This article presents to detect lung tumor, classification and area recognition system. Nowadays, Lung tumor is major cause of death for all the people. Early detection of lung tumor plays an important part to enhance chance for survive to live. Early detecting of tumor is a important role for the treatment where Computed Tomography (CT) screening are consider as appropriate method for detecting the...
This paper presented an approach used Fully Convolutional Networks (FCN) to segment liver tumor in Computed Tomography (CT) images. In addition, using different characteristics of scan quality and tumor conspicuity among portal venous phase, arterial phase and equilibrium phase, we proposed an automatic liver tumor segmentation with Multiple Kernel Fully Convolutional Networks (MK-FCN). MK-FCN can...
Kidney disease is one of the life threatening diseases prevailing among the humans. Most of the people die because of kidney diseases. It occurs due to the change which is occurring in the production of DNA cells (cancer), protein deficiency (nephritis) etc., In this paper, an automatic detection of the kidney diseases from CT abdominal images is proposed. First, the CT abdominal images are acquired...
Traumas and illnesses can cause injury in internal organs. The liver, being the largest abdominal organ, is most likely to be injured by trauma. Currently CT scans are analyzed by radiologists to see if there is any injuries in organs; however, due to the large amounts of data and its complexity in terms of noise, intensity variations in different images and so on, visual inspection would be time...
Lung vessel segmentation of computed tomography (CT) images is important in clinical practise and challenging due to difficulties associated with minor size and blurred edges of lung vessels. A vessel segmentation method is proposed for lung images based on a random forest classifier and sparse auto-encoder features. First, the multi-scale representations of lung images are obtained using the Gaussian...
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