BackgroundSegmented service delivery with consequent inefficiencies in health systems was one of the main concerns raised during scaling up of disease-specific programs in the last two decades. The organized response to NCD is in infancy in most LMICs with little evidence on how the response is evolving in terms of institutional arrangements and policy development processes.MethodsDrawing on qualitative review of policy and program documents from five LMICs and data from global key-informant surveys conducted in 2004 and 2010, we examine current status of governance of response to NCDs at national level along three dimensions— institutional arrangements for stewardship and program management and implementation; policies/plans; and multisectoral coordination and partnerships.ResultsSeveral positive trends were noted in the organization and governance of response to NCDs: shift from specific NCD-based programs to integrated NCD programs, increasing inclusion of NCDs in sector-wide health plans, and establishment of high-level multisectoral coordination mechanisms.Several areas of concern were identified. The evolving NCD-specific institutional structures are being treated as ‘program management and implementation’ entities rather than as lead ‘technical advisory’ bodies, with unclear division of roles and responsibilities between NCD-specific and sector-wide structures. NCD-specific and sector-wide plans are poorly aligned and lack prioritization, costing, and appropriate targets. Finally, the effectiveness of existing multisectoral coordination mechanisms remains questionable.ConclusionsThe ‘technical functions’ and ‘implementation and management functions’ should be clearly separated between NCD-specific units and sector-wide institutional structures to avoid duplicative segmented service delivery systems. Institutional capacity building efforts for NCDs should target both NCD-specific units (for building technical and analytical capacity) and sector-wide organizational units (for building program management and implementation capacity) in MOH.The sector-wide health plans should reflect NCDs in proportion to their public health importance. NCD specific plans should be developed in close consultation with sector-wide health- and non-health stakeholders. These plans should expand on the directions provided by sector-wide health plans specifying strategically prioritized, fully costed activities, and realistic quantifiable targets for NCD control linked with sector-wide expenditure framework. Multisectoral coordination mechanisms need to be strengthened with optimal decision-making powers and resource commitment and monitoring of their outputs.

Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm.

Diffusion magnetic resonance imaging (dMRI) allows to reconstruct the main pathways of axons within the white matter of the brain as a set of polylines, called streamlines. The set of streamlines of the whole brain is called the tractogram. Organizing tractograms into anatomically meaningful structures, called tracts, is known as the tract segmentation problem, with important applications to neurosurgical planning and tractometry. Automatic tract segmentation techniques can be unsupervised or supervised. A common criticism of unsupervised methods, like clustering, is that there is no guarantee to obtain anatomically meaningful tracts. In this work, we focus on supervised tract segmentation, which is driven by prior knowledge from anatomical atlases or from examples, i.e., segmented tracts from different subjects. We present a supervised tract segmentation method that segments a given tract of interest in the tractogram of a new subject using multiple examples as prior information. Our proposed tract segmentation method is based on the idea of streamline correspondence i.e., on finding corresponding streamlines across different tractograms. In the literature, streamline correspondence has been addressed with the nearest neighbor (NN) strategy. Differently, here we formulate the problem of streamline correspondence as a linear assignment problem (LAP), which is a cornerstone of combinatorial optimization. With respect to the NN, the LAP introduces a constraint of one-to-one correspondence between streamlines, that forces the correspondences to follow the local anatomical differences between the example and the target tract, neglected by the NN. In the proposed solution, we combined the Jonker-Volgenant algorithm (LAPJV) for solving the LAP together with an efficient way of computing the nearest neighbors of a streamline, which massively reduces the total amount of computations needed to segment a tract. Moreover, we propose a ranking strategy to merge correspondences coming from different examples. We validate the proposed method on tractograms generated from the human connectome project (HCP) dataset and compare the segmentations with the NN method and the ROI-based method. The results show that LAP-based segmentation is vastly more accurate than ROI-based segmentation and substantially more accurate than the NN strategy. We provide a Free/OpenSource implementation of the proposed method.

The Energy hole problem, a common phenomenon in wireless sensor networks, significantly decreases the lifetime of any deployed network. Some of the popular techniques to minimize such problems are using mobile sinks instead of static sinks, extending the transmission range dynamically, and deploying redundant sensor nodes near the base station/sink. The major drawback to these techniques are that energy holes may still be created at some point due to their static nature of deployment, despite having the overall residual energy very high. In this research work, we adopt a new approach by dividing the whole network into equiangular wedges and merging a wedge with its neighboring wedge dynamically whenever individual residual energy of all member nodes of a wedge fall below a threshold value. We also propose an efficient Head Node (HN) selection scheme to reduce the transmission energy needed for forwarding data packets among Head Nodes. Simulation results show that WEMER, our proposed WEdge MERging based scheme, provides significantly higher lifetime and better energy efficiency compared to state-of-the-art Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and contemporary Concentric Clustering Scheme (CCS), and Multilayer Cluster Designing Algorithm (MCDA).