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Transcranial high-intensity focused ultrasound has recently been used to noninvasively treat several types of brain disorders. However, due to the large differences in acoustic properties of skulls and the surrounding soft tissue, it can be a challenge to adequately focus an ultrasonic beam through the skull. We present a novel, fast, full-wave method of correcting the aberrations caused by the skull...
Craniosynostosis, a disorder in which one or more fibrous joints of the skull fuse prematurely, causes skull malformation and may be associated with increased intracranial pressure and developmental delays. In order to perform medical research studies that relate phenotypic abnormalities to outcomes such as cognitive ability or results of surgery, biomedical researchers need an automated methodology...
In this study we analyze lung shape change between the upright and supine postures and the effect of this shape change on the deformation of lung tissue under gravity. We use supine computed tomography images along with upright tomosynthesis images obtained on the same day to show that there is significant diaphragmatic movement between postures. Using a continuum model of lung tissue deformation...
In Deep Brain Stimulation (DBS), the location of implanted electrodes in the brain has direct influence on the therapeutic effect of the treatment. This work deals with estimating the position of the implanted DBS electrodes from the images registered by X-ray Computed Tomography (CT) scanners. A technique named junction method that takes advantage of the streak artifacts created by the metal parts...
Radiation dose of X-ray Computed Tomography (CT) imaging has raised a worldwide health concern. Therefore, low-dose CT imaging has been of a huge interest in the last decade. However, lowering the radiation dose degrades the image quality by increasing the noise level, which may reduce the diagnostic performance of the images. As a result, image denoising is one of the fundamental tasks in low-dose...
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies. Manual annotation, current gold standard, is time consuming and often subject to human bias. On the other hand, current state-of-the-art...
In this paper, a framework for the inflation of micro-CT data using intravascular ultrasound (IVUS) images, is presented. The proposed methodology consists of four steps. In the first step a centerline is extracted from the micro-CT images. In the second step the micro CT images are segmented automatically using the k-means algorithm. In the third step IVUS- micro-CT images are co-registered based...
Energy resolved photon-counting detectors could achieve more than one spectral measurement. The goal of this study is to investigate, with experiment, the ability to decompose five materials using energy discriminating detectors and multiple discriminant analysis (MDA). A small field-of-view multi-energy CT system was built. Linear attenuation coefficient was considered as features of multiple energy...
Combined positron emission tomography and computed tomography (PET-CT) produces functional data (from PET) in relation to anatomical context (from CT) and it has made a major contribution to improved cancer diagnosis, tumour localisation, and staging. The ability to retrieve PET-CT images from large archives has potential applications in diagnosis, education, and research. PET-CT image retrieval requires...
Fluorodeoxyglucose positron emission tomography — computed tomography (FDG PET-CT) is the preferred image modality for lymphoma diagnosis. Sites of disease generally appear as foci of increased FDG uptake. Thresholding methods are often applied to robustly separate these regions. However, its main limitation is that it also includes sites of FDG excretion and physiological FDG uptake regions, which...
Computed tomography (CT) is the current gold standard for imaging kidney stones, albeit at the cost of radiation exposure. Conventional magnetic resonance imaging (MRI) sequences are insensitive to detecting the stones because of their appearance as a signal void. With the development of 2D ultra-short echo-time (UTE) MRI sequences, it becomes possible to image kidney stones in vitro. In this work,...
This paper describes the creation of an anatomically detailed high resolution model of the human head based on the Visible Human Female data from the National Library of Medicine archives. Automatic and semi-automatic segmentation algorithms were applied over the 3 image volumes — CT, MRI and anatomical cryo-sections of the cadaver — to label a total of 23 tissues. The results were combined to create...
Rib cage structure and morphology is important for anatomical analysis of chest CT scans. A fundamental challenge in rib cage extraction is varying intensity levels and connection with adjacent bone structures including shoulder blade and sternum. In this study, we present a fully automated 3-D algorithm to segment the rib cage by detection and separation of other bone structures. The proposed approach...
In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are currently identified manually on MRI to identify reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures...
Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. These augmented glasses uncover...
Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves...
This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast...
In this paper, we present a new active contours model to segment human leg bones in computed tomography images that is based on a variable-weighted combination of local and global intensity. This model can split an object surrounded by both weak and strong boundaries, and also distinguish very adjacent objects with those boundaries. The ability of this model is required for segmentation in medical...
Information about abdominal wall can be used for many applications from organ segmentation, registration, and surgical simulation. The challenges exist in abdominal wall extraction due to its varieties in shapes, connection to the internal organs and anterior layer edge formed between the muscle and fascia/fatty layer, which may distract the shape model. In this paper we present an approach to the...
Computational studies of aortic hemodynamics require accurate and reproducible segmentation of the aortic tree from whole body, contrast enhanced CT images. Three methods were vetted for segmentation. A semi-automated approach that utilizes denoising, the extended maxima transform, and a minimal amount of manual segmentation was adopted.
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