The results indicate that the proposed technique yields reproducible and accurate guidewire positions within a short, clinically relevant time frame. These calculated positions may be useful in facilitating neurovascular interventions.

In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0–1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.

Endovascular interventional procedures are being used more frequently in cardiovascular surgery. Unfortunately, procedural failure, e.g., vessel dissection, may occur and is often related to improper guidewire and/or device selection. To support the surgeon's decision process and because of the importance of the guidewire in positioning devices, we propose a method to determine the guidewire path prior to insertion using a model of its elastic potential energy coupled with a representative graph construction.The 3D vessel centerline and sizes are determined for a specified vessel. Points in planes perpendicular to the vessel centerline are generated. For each pair of consecutive planes, a vector set is generated which joins all points in these planes. We construct a graph representing these vector sets as nodes. The nodes representing adjacent vector sets are joined by edges with weights calculated as a function of the angle between the corresponding vectors (nodes). The optimal path through this weighted directed graph is then determined using shortest path algorithms, such as topological sort based shortest path algorithm or Dijkstra's algorithm. Volumetric data of an internal carotid artery phantom (Ø 3.5mm) were acquired. Several independent guidewire (Ø 0.4mm) placements were performed, and the 3D paths were determined using rotational angiography.The average RMS distance between the actual and the average simulated guidewire path was 0.7mm; the computation time to determine the path was 3 seconds. The ability to predict the guidewire path inside vessels may facilitate calculation of vessel-branch access and force estimation on devices and the vessel wall.

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In this paper, we describe a set of algorithms and an implementation (called VolEdit), for interactively manipulating 3D volumetric objects (datasets). The system utilizes skeletons, which allows users/animators to interactively and intuitively specify the location and type of deformation desired. The skeleton is extracted automatically from the volumetric model and indexes the appropriate part of the volume that needs to be transformed by defining piecewise bounds of the volume. The deformed volume is then reconstructed and rendered using commodity graphics cards. The system performs in real-time with near-interactive speeds. The VolEdit system is demonstrated with two volumes, the Visible Human Dataset and a colon MRI dataset.

We propose an optimization algorithm to solve the brachytherapy seed localization problem in prostate brachytherapy. Our algorithm is based on novel geometric approaches to exploit the special structure of the problem and relies on a number of key observations which help us formulate the optimization problem as a minimization integer program (IP). Our IP model precisely defines the feasibility polyhedron for this problem using a polynomial number of half-spaces; the solution to its corresponding linear program is rounded to yield an integral solution to our task of determining correspondences between seeds in multiple projection images. The algorithm is efficient in theory as well as in practice and performs well on simulation data (approximately 98% accuracy) and real X-ray images (approximately 95% accuracy). We present in detail the underlying ideas and an extensive set of performance evaluations based on our implementation.

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The main objective of this paper is to perform data analysis of ground based measurement and review the state of the art of IIT Jodhpur Rooftop solar photovoltaic installed 101 kW system. Solar power forecasting is playing a key role in solar PV park installation, operation and accurate solar power dispatchability as well as scheduling. Solar Power varies with time and geographical locations and meteorological conditions such as ambient temperature, wind velocity, solar radiation and module temperature. The location of Solar PV system is the main reason of solar power variability. Solar variability totally depends on system losses (deterministic losses) and weather parameter (stochastic losses). In the case of solar power, deterministic losses can be found out accurately but stochastic losses are very uncertain and unpredicted in nature. The proposed soft computing technique will be suitable for solar power forecasting modeling. In this paper Fuzzy theory, Adaptive Neuro-fuzzy interface system, artificial neural network and generalized neural network are used as powerful tool of solar power Forecasting. This soft computing cum nature inspired techniques are able to accurately and fast forecasting compared to conventional methods of forecasting. This is done analyzing the operational data of 101 kW PV systems (43.30 kW located in Block 1 and 58.08 kW in Block 2), during the year 2011.