The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a classification-driven biomedical image retrieval approach based on multi-class support vector machine (SVM) and uses image filtering and similarity fusion. In this framework, the probabilistic outputs of the SVM are exploited to reduce the search space for similarity matching. In addition, the predicted category of the query image is used for linear combination of similarity...
Image-based disease diagnosis often requires radiologists' qualitative interpretations that are highly dependent on their levels of expertise, and physical and mental status. Automated image analysis tools can help radiologists increase the diagnosis accuracy by providing quantitative measures. Accordingly, this paper presents an automated method for dementia diagnosis using search and retrieval of...
The extraction of the brain portion of a neurological image is often necessary prior to tissue segmentation or image registration. While MR Imaging studies on the rat have gained much interest lately, an automatic and robust rat brain extraction tool is still lacking. In this paper, we present a deformable surface model-based rat brain extraction method which extends the popular human brain extraction...
Fully automated high-throughput and high-content experiments need reusable general modules, that can be combined in a flexible way to build the solution. Even though the biological objects or structures may not share any common features, the transformations that act on the structures (like rotations, translations or deformations) are nearly the same in every experiment. In the talk I will show general...
Prostate cancer is considered to be one of the main causes of cancer related death for men in the United States. Automated methods for prostate cancer localization based on multispectral magnetic resonance imaging (MRI) haver recently emerged as a non invasive technique for this purpose as an alternative to transrectal ultrasound. However, the automated methods developed to this date require a manual...
Minimally invasive trans-catheter aortic valve implantation (TAVI) procedure can be greatly facilitated using smart visualization and guidance technology involving the 3-D model of the aorta. In this paper, a hybrid method is proposed for contrast-based registration between the 3-D model and angiography of the aorta during TAVI procedures. By integrating the information of aorta segmentation and aortic...
In this paper we propose a new scheme for measuring regional ventilation from tagged hyperpolarized helium-3 MR images. A new registration cost function that incorporates both the intensity information (SSD) and the shape feature (SSBMD) from the images is proposed for registering end inspiration to the end expiration image. The smoothness of the displacement field is maintained by incorporating the...
Establishing correspondences across structural and functional brain images via labeling, or parcellation, is an important and challenging task for clinical neuroscience and cognitive psychology. A limitation with existing approaches is that they i) possess shallow architectures, ii) are based on heuristic manual feature engineering, and iii) assume the validity of the designed feature model. In contrast,...
In this paper we use humans and chimpanzees brain MRI databases to develop methods for evaluating global brain asymmetries. We perform brain segmentation and hemispheric surface extraction on both populations. The human brain segmentation pipeline is adapted to chimpanzees in order to obtain results of good quality. To alleviate the problems due to cortical variability we propose a mesh processing...
Spherical Functions (SF) play a pivotal role in Diffusion MRI (dMRI) in representing sub-voxel-resolution micro-architectural information of the underlying tissue. This information is encoded in the geometric shape of the SF. In this paper we use a polynomial approach to extract geometric characteristics from SFs in dMRI such as the maxima, minima and saddle-points. We then use differential geometric...
Decoding perceptual or cognitive states based on brain activity measured using functional Magnetic Resonance Imaging (fMRI) can be achieved using machine learning algorithms to train classifiers of specific stimuli. However, the high dimensionality and intrinsically low Signal-to-Noise Ratio (SNR) of fMRI data poses great challenges to such techniques. The problem is aggravated in the case of multiple...
In this paper, we described and validated a semi-automated algorithm based on the level set method to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) of the carotid arteries from 3D ultrasound (3DUS) images to support the computation of carotid vessel wall volume (VWV). We incorporated local region-based and edge-based energies for the MAB segmentation, and both local and...
A fetal ultrasound (US) biometry plane can be identified from the presence and absence of landmarks in the image. We propose an automated method of detecting two important anatomical landmarks (stomach bubble and umbilical vein) from the fetal ultrasound abdomen scan for the purpose of scoring the image quality. The implementation is based on the AdaBoost learning algorithm with an execution time...
Although feature selection has been proven to be very effective in machine learning and pattern classification applications, it has not been widely practiced in the area of image annotation and retrieval. This paper presents a method of selecting a near optimal to optimal subset of statistical texture descriptors in efficient representation and retrieval of ultrasound medical images. An objective...
Diagnosis of white matter damage by neonatal cranial ultrasound (CrUS) is subject to inter-observer variability and has a low sensitivity to detect late abnormal neurodevelopment in life. In the last decades there have been a significant effort reporting that statistical features of ultrasound images carry important information associated with changes of tissue microstructure. In this work we explored...
We propose an automated method to segment cortical necrosis from brain FLAIR-MR Images. Cortical necrosis are regions of dead brain tissue in the cortex caused by cerebrovascular disease (CVD). The accurate segmentation of these regions is difficult as their intensity patterns are similar to the adjoining cerebrospinal fluid (CSF). We generate a model of normal variation using MR scans of healthy...
A fast method is proposed for DTI neural tract extraction and visualization. Existing efficient tract extraction approaches are based on localizing fibers from a pre-computed whole brain tractography. However, the tracking parameters of precomputed fibers cannot be easily adjusted quickly. Moreover, high noise in the estimated orientation around crossing and neighboring tracts often diverts fibers...
Whole brain extraction is an important pre-processing step in neuro-image analysis. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to...
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...
The use of multi-parametric Magnetic Resonance Imaging (T2-weighted, MR Spectroscopy (MRS), Diffusion-weighted (DWI)) has recently shown great promise for diagnosing and staging prostate cancer (CaP) in vivo. Such imaging has also been utilized for evaluating the early effects of radiotherapy (RT) (e.g. intensity-modulated radiation therapy (IMRT), proton beam therapy, brachytherapy) in the prostate...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.