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Medical text mining has gained increasing interest in recent years. Radiology reports contain rich information describing radiologist's observations on the patient's medical conditions in the associated medical images. However, as most reports are in free text format, the valuable information contained in those reports cannot be easily accessed and used,(More)
A method for automatic classification of computed tomography (CT) brain images of different head trauma types is presented in this paper. The method has three major steps: 1. The images are first segmented to find potential hemorrhage regions using ellipse fitting, background removal and wavelet decomposition technique; 2. For each region, features (such as(More)
BACKGROUND This study aimed to evaluate 2 commonly used posterior approach entry points for ventricular cannulation and the ideal trajectories using 3-dimensional virtual reality technology. METHODS Magnetic resonance imaging data of 10 patients without gross ventricular dilatation or distortion were retrieved and reconstructed. A stereoscopic(More)
Multi-slice Computer Tomography (CT) scans are widely used in today's diagnosis of head traumas. It is effective to disclose the bleeding and fractures. In this paper, we present an automated detection of CT scan slices which contain hemorrhages. Our method is robust towards various rotation, displacement and motion blur. Detection of these pathological(More)
Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction.(More)
Automatic medical image classification is difficult because of the lacking of training data. As manual labeling is too costly, we provide an automatic labeling solution to this problem by making use of the radiology report associated with the medical images. We first segment and reconstruct the 3D regions of interest (ROIs) from the medical images, and(More)
Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be observed most clearly and uses automatically detected(More)
In intracranial pathological examinations using CT scan, brain midline shift (MLS) is an important diagnostic feature indicating the pathological severity and patient's survival possibility. In this paper, we develop a new method of tracing the brain midline shift in traumatic brain injury (TBI) CT images using its original cause-the hemorrhage. Firstly, we(More)
OBJECTIVE To assess the utility of pre-operative 3-dimension (3D) visualisation and surgical planning with the Dextroscope in combination with the use of DEX-Ray-a novel augmented reality surgical navigation platform for resection of meningiomas in the falcine, convexity and parasagittal regions. METHODS AND RESULTS Magnetic resonance imaging (MRI) and(More)
Large number of medical images are produced daily in hospitals and medical institutions, the needs to efficiently process, index, search and retrieve these images are great. In this paper, we propose a pathology-based medical image annotation framework using a statistical machine translation approach. After pathology terms and regions of interest (ROIs) are(More)