Region of interest
About: Region of interest is a research topic. Over the lifetime, 7895 publications have been published within this topic receiving 104479 citations. The topic is also known as: ROI.
Papers published on a yearly basis
TL;DR: A toolbox called MarsBar is implemented for region of interest analysis within the SPM99 software package, which may have many advantages in terms of statistical power and the ease of interpretation of neuroimaging data.
Abstract: Most functional imaging studies use analyses that look for effects anywhere in the brain. The standard approach is to calculate a statistic relating the experimental effect of interest to the data for each brain voxel. This method has the advantage that it can detect strong effects without apriori constraint on the area that activation will occur. Problems arise when we wish to ask the question whether a particular brain area has been activated: if we know the shape and location of the expected activity, then voxel by voxel approaches have low power, because of the multiple comparisons across voxels. Whole brain analyses usually use image smoothing in order to increase signal to noise; however, the best smoothing filter will depend on the shape of the activation, which may well not be matched by a standard kernel such as a Gaussian. The most direct answer to the question "has this area been activated" is to use a region of interest analysis. Here we define a region, and perform the statistical test on the mean time course of the voxels within the region. The contribution of the voxels may be weighted using the expected shape of the activation. This approach has two advantages. First, we increase power by avoiding the multiple comparison problem. Second, if we are correct about the shape and location of the region, the process of taking the mean is equivalent to using the best smoothing kernel to recover the signal. This method has proved very powerful in analysing the activation in well defined regions. For example, Kanwisher et al have used screening tasks and voxel by voxel statistics to define regions of interest, and used these regions to investigate the nature of the original activation in further experiments of visual analysis of faces, scenes, objects and body parts. We have implemented a toolbox called "MarsBar" for region of interest analysis within the SPM99 software package (available for free download from http://www.mrc-cbu.cam.ac.uk/Imaging/marsbar.html). The user can define regions using activations from previous SPM analyses, binary or weighted images, or simple shapes (boxes or spheres). Regions can be combined using a full range of algebra to give new regions. Functions include overlap between regions (logical and) or combination (logical or). The software can then extract raw or filtered time courses from the region for futher analysis outside SPM. It can also use new or previous SPM analysis files to analyze the regional time course. t or F statistics for multiple regions can be computed, and the results plotted using the SPM graphical interface. We are currently working on estimation of percentage signal change for the region data. Region of interest analyses are likely to become more important as prior hypotheses about the location of activation become more specific. This may have many advantages in terms of statistical power and the ease of interpretation of neuroimaging data. We hope that this toolbox will make such analyses simpler to implement.
TL;DR: A component based method for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented and the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced.
Abstract: A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.
26 Dec 2007
TL;DR: It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech-256.
Abstract: We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i) shape and appearance representations that support spatial pyramid matching over a region of interest. This generalizes the representation of Lazebnik et al., (2006) from an image to a region of interest (ROI), and from appearance (visual words) alone to appearance and local shape (edge distributions); (ii) automatic selection of the regions of interest in training. This provides a method of inhibiting background clutter and adding invariance to the object instance 's position; and (iii) the use of random forests (and random ferns) as a multi-way classifier. The advantage of such classifiers (over multi-way SVM for example) is the ease of training and testing. Results are reported for classification of the Caltech-101 and Caltech-256 data sets. We compare the performance of the random forest/ferns classifier with a benchmark multi-way SVM classifier. It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech-256.
TL;DR: A new computational approach permits rapid analysis and visualization of myocardial strain within 5–10 min after the scan is complete, and its performance is demonstrated on MR image sequences reflecting both normal and abnormal cardiac motion.
Abstract: The present invention relates to a method of measuring motion of an object such as a heart by magnetic resonance imaging. A pulse sequence is applied to spatially modulate a region of interest of the object and at least one first spectral peak is acquired from the Fourier domain of the spatially modulated object. The inverse Fourier transform information of the acquired first spectral-peaks is computed and a computed first harmonic phase image is determined from each spectral peak. The process is repeated to create a second harmonic phase image from each second spectral peak and the strain is determined from the first and second harmonic phase images. In a preferred embodiment, the method is employed to determine strain within the myocardium and to determine change in position of a point at two different times which may result in an increased distance or reduced distance. The method may be employed to determine the path of motion of a point through a sequence of tag images depicting movement of the heart. The method may be employed to determine circumferential strain and radial strain.
TL;DR: A new transform is presented that utilizes local radial symmetry to highlight points of interest within a scene and is seen to offer equal or superior performance to contemporary techniques at a relatively low-computational cost.
Abstract: A new transform is presented that utilizes local radial symmetry to highlight points of interest within a scene. Its low-computational complexity and fast runtimes makes this method well-suited for real-time vision applications. The performance of the transform is demonstrated on a wide variety of images and compared with leading techniques from the literature. Both as a facial feature detector and as a generic region of interest detector the new transform is seen to offer equal or superior performance to contemporary techniques at a relatively low-computational cost. A real-time implementation of the transform is presented running at over 60 frames per second on a standard Pentium III PC.
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