Current Issue : January - March Volume : 2012 Issue Number : 1 Articles : 6 Articles
Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications....
Aim. To determine the level of the conus medullaris-Tuffier�s line, and conus medullaris-Tuffier�s line distance using imaging and evaluate their relation to age and gender.Methods.We performed a cross-sectional study of 189 adult participants, who underwent MR imaging of lumbosacral spine. Each vertebra was divided into 3 equal segments (upper, middle, and lower), and intervertebral disc space was also assumed as one segment. All segments from T12 upper segment to L5S1 intervertebral disc were numbered consecutively. The position of conus medullaris and Tuffier�s line was determined by the vertebral segment or intervertebral disc space at the same level. The patients were stratified into high/low conus medullaris position (cutpoint: L1 middle segment) and short/long conus Tuffier�s distance (cutpoint: 14 segments). Results.Women with low conus were significantly more than men, in patients older than 50 years old (72.7% in females versus 55.3% in males;P < .05), whereas there was not such a sexual dimorphism\r\nin patients younger than 50 years old. Similarly, short conus-Tuffier�s distance was more frequent among women than men in patients older than 50 years old (59.7% in females versus 39.5% in males; P < .05), whereas there was not any gender difference in patients younger than 50 years old. Conus-Tuffier�s distance was negatively correlated with age (r = -0.32, P < .001) in all studied population. Conclusion. Anatomical landmarks vary according to age and gender, with a lower end of conus medullaris in women, so clinicians should use more caution on the identification of the appropriate site for lumbar puncture, particularly in elderly women....
Single photon emission computed tomography (SPECT) imaging is widely implemented in nuclear medicine as its clinical role in the diagnosis and management of several diseases is, many times, very helpful (e.g., myocardium perfusion imaging). The quality of SPECT images are degraded by several factors such as noise because of the limited number of counts, attenuation, or scatter of photons. Image filtering is necessary to compensate these effects and, therefore, to improve image quality. The goal of filtering in tomographic images is to suppress statistical noise and simultaneously to preserve spatial resolution and contrast. The aim of this work is to describe the most widely used filters in SPECT applications and how these affect the image quality. The choice of the filter type, the cut-off frequency and the order is a major problem in clinical routine. In many clinical cases, information for specific parameters is not provided, and findings cannot be extrapolated to other similar SPECT imaging applications. A literature review for the determination of the mostly used filters in cardiac, brain, bone, liver, kidneys, and thyroid applications is also presented. As resulting from the overview, no filter is perfect, and the selection of the proper filters, most of the times, is done empirically. The standardization of image-processing results may limit the filter types for each SPECT examination to certain few filters and some of their parameters. Standardization, also, helps in reducing image processing time, as the filters and their parameters must be standardised before being put to clinical use. Commercial reconstruction software selections lead to comparable results interdepartmentally. The manufacturers normally supply default filters/parameters, but these may not be relevant in various clinical situations. After proper standardisation, it is possible to use many suitable filters or one optimal filter....
Inverse inference has recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, called Multiclass Sparse Bayesian Regression (MCBR), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features....
Medical imaging system simulators are tools that provide a means to evaluate system architecture and create artificial image sets that are appropriate for specific applications. We have modified SIMRI, a Bloch equation-based magnetic resonance image simulator, in order to successfully generate high-resolution 3D MR images of the Montreal brain phantom using Blue Gene/L systems. Results show that redistribution of the workload allows an anatomically accurate 2563 voxel spin-echo simulation in less than 5 hours when executed on an 8192-node partition of a Blue Gene/L system....
Background\r\nThis study presents a semiautomated approach for volumetric analysis of lung tumors and evaluates the feasibility of using volumes as an alternative to line lengths as a basis for response evaluation criteria in solid tumors (RECIST). The overall goal for the implementation was to accurately, precisely, and efficiently enable the analyses of lesions in the lung under the guidance of an operator. Methods. An anthropomorphic phantom with embedded model masses and 71 time points in 10 clinical cases with advanced lung cancer was analyzed using a semi-automated workflow. The implementation was done using the Cognition Network Technology. Results. Analysis of the phantom showed an average accuracy of 97%. The analyses of the clinical cases showed both intra- and interreader variabilities of approximately 5% on average with an upper 95% confidence interval of 14% and 19%, respectively. Compared to line lengths, the use of volumes clearly shows enhanced sensitivity with respect to determining response to therapy. Conclusions. It is feasible to performvolumetric analysis efficiently with high accuracy and low variability, even in patients with late-stage cancer who have complex lesions....
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