Current Issue : April - June Volume : 2015 Issue Number : 2 Articles : 5 Articles
Nonconvex optimization has shown that it needs substantially fewer measurements than l1 minimization for exact recovery under\nfixed transform/overcomplete dictionary. In this work, two efficient numerical algorithms which are unified by the method named\nweighted two-level Bregman method with dictionary updating (WTBMDU) are proposed for solving l???? optimization under the\ndictionary learning model and subjecting the fidelity to the partial measurements. By incorporating the iteratively reweighted\nnorminto the two-level Bregman iteration method with dictionary updating scheme (TBMDU), themodified alternating direction\nmethod (ADM) solves the model of pursuing the approximated l????-norm penalty efficiently. Specifically, the algorithms converge\nafter a relatively small number of iterations, under the formulation of iteratively reweighted l1 and l2 minimization. Experimental\nresults on MR image simulations and real MR data, under a variety of sampling trajectories and acceleration factors, consistently\ndemonstrate that the proposed method can efficiently reconstructMR images fromhighly undersampled k-space data and presents\nadvantages over the current state-of-the-art reconstruction approaches, in terms of higher PSNR and lower HFEN values...
The cornea is the front of the eye. Its inner cell layer, called the endothelium, is important because it is closely related to the light\ntransparency of the cornea. An in vivo observation of this layer is performed by using specular microscopy to evaluate the health of\nthe cells: a high spatial density will result in a good transparency.Thus, the main criterion required by ophthalmologists is the cell\ndensity of the cornea endothelium, mainly obtained by an image segmentation process. Different methods can perform the image\nsegmentation of these cells, and the three most performingmethods are studied here. The question for the ophthalmologists is how\nto choose the best algorithm and to obtain the best possible results with it. This paper presents a methodology to compare these\nalgorithms together. Moreover, by the way of geometric dissimilarity criteria, the algorithms are tuned up, and the best parameter\nvalues are thus proposed to the expert ophthalmologists....
Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may\nhelp to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the\nintracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to\nnormal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV\nestimation problem challenging. In this paper, we present a new approach to performICV extraction based on the use of a library of\nprelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based\non BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent\nstate-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while\nmaintaining a reduced computational burden....
Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process\nbut suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach\nfor initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas\nconsisting of a set of representative lung features and an average lung shape.The ASM pose parameters are found by transforming\nthe average lung shape based on an affine transform computed from matching features between the new image and representative\nlung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 �± 0.068 for initialization and\n0.974 �± 0.017 for subsequent segmentation, based on an independent reference standard.The mean absolute surface distance error\nwas 0.948 �± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared\nto four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based\nsegmentation....
When skeletal muscle fibres shorten, they must increase in their transverse dimensions in order to maintain a constant volume.\nIn pennate muscle, this transverse expansion results in the fibres rotating to greater pennation angle, with a consequent reduction\nin their contractile velocity in a process known as gearing. Understanding the nature and extent of this transverse expansion is\nnecessary to understand the mechanisms driving the changes in internal geometry of whole muscles during contraction. Current\nmethodologies allow the fascicle lengths, orientations, and curvatures to be quantified, but not the transverse expansion. The purpose\nof this study was to develop and validate techniques for quantifying transverse strain in skeletalmuscle fascicles during contraction\nfrom B-mode ultrasound images. Images were acquired from the medial and lateral gastrocnemii during cyclic contractions,\nenhanced using multiscale vessel enhancement filtering and the spatial frequencies resolved using 2D discrete Fourier transforms.\nThe frequency information was resolved into the fascicle orientations that were validated against manually digitized values. The\ntransverse fascicle strains were calculated from their wavelengths within the images. These methods showed that the transverse\nstrain increases while the longitudinal fascicle length decreases; however, the extent of these strains was smaller than expected....
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