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Objective To describe the effectiveness and security of DSA-guided percutaneous sclerotherapy for venous malformations of penile region in children. Methods A total of 9 cases children with penile venous malformations treated by DSA-guided percutaneous sclerotherapy from January 2017 to June 2018 were enrolled in this study. Local angiography was performed under DSA fluoroscopy, and the shape, extent and venous drainage of the tumor nest were judged. Then sclerosing agents (Pingyangmycin and Polidocanol) were selected according to the flow rate of angiography and treated by percutaneous injection under DSA monitoring. Imaging examination (MRI) was performed to evaluate clinical improvement. Results A total of 26 interventional sclerotherapy sessions were performed in 9 children, with an average of (2.3 ± 0.5) sessions per case. After a mean follow-up of 7.3 months, 6 cases were cured, and 3 cases were in basic remission. There were 9 cases of temporary edema secondary to the treatment site and 3 cases of mild pain. No serious complications such as ulceration, hemorrhage, infection and dysfunction occurred in any patients. Conclusion DSA-guided percutaneous sclerotherapy for venous malformations of penile region in children is safe and effective, does not affect the appearance and function of penis, and is worthy of clinical promotion. Type of study Treatment Study. Level of evidence Level IV, Case series with no comparison group.

PURPOSE To report the effectiveness and safety of transcatheter arterial sclerosing embolization (TASE) for the treatment of parotid infantile hemangiomas that did not respond appreciably to propranolol. MATERIALS AND METHODS A total of 21 infants (12 male and 9 female) with large propranolol-resistant infantile hemangiomas in the parotid region were enrolled in this study. During TASE, the feeding arteries of the lesions were embolized using pingyangmycin-lipiodol emulsion and polyvinyl alcohol particles (300-500 μm) to reduce the blood flow rate. All children were followed up as outpatients at 2 weeks and monthly thereafter. The curative effect was evaluated at the 1- and 3-month follow-up visits. RESULTS Nine lesions were located on the right side of the parotid gland, whereas 12 were located on the left side. The feeding arteries in all patients originated from branches of the external carotid artery. TASE was technically successful in all patients. The mean (± SD) maximal diameter of the hemangiomas significantly decreased from 6.50 cm ± 2.28 before treatment to 3.56 cm ± 1.84 at 1 month after TASE (P

Infantile haemangioma (IH) is a benign vascular tumour type that occurs in 3-10% of infants. In the present meta-analysis, previous studies comparing clinical outcomes, including the recovery rate and haemangioma activity score (HAS), adverse effects and relapse rates, were compared between patients treated with atenolol and those treated with propranolol for IH. A systematic search in various databases, including Medline, Cochrane Controlled Register of Trials, ScienceDirect and Google Scholar from inception until July 2019 was performed. The Cochrane risk of bias tool was used to assess the quality of published trials. A meta-analysis with a random-effects model and reported pooled mean differences (MD) or odds ratios (OR) with 95% CIs was performed. In total, 8 studies including 608 participants were analyzed. Only 2 studies were randomized controlled trials, while the majority of studies had low or unclear bias risks. Except for the response to medication (pooled OR=1.49; 95% CI, 0.85-2.18), all other outcomes (HAS, adverse reactions and relapse rate) were better for the atenolol group than the propranolol group. Atenolol resulted in better HAS (pooled MD=0.16; 95% CI, -0.42 to 0.73). Propranolol had more adverse reactions (pooled OR=2.17; 95% CI, 0.93-5.06) and a higher relapse rate (pooled OR, 1.67; 95% CI, 0.44-6.41) when compared to atenolol. However, these findings were not statistically significant. The results of this analysis suggest that atenolol may be non-inferior to propranolol and may offer advantages, including lower adverse reactions and relapse rates.

BackgroundExtensive and complex head and neck lymphatic malformations (LMs) are challenging to manage through traditional therapy. The purpose of this retrospective study was to assess the efficacy and safety of sirolimus in infants with refractory head and neck LMs.MethodsSirolimus was administered orally on a continuous dosing schedule. Patients were seen every month for the first three months and then subsequently every three months. The primary endpoints were safety and efficacy based on clinical and radiological evaluations.ResultsEight patients, refractory to standard care, were enrolled and received sirolimus continuously. After 12 months of follow-up, the response and safety to medication was evaluated: all patients experienced reductions in LMs bulk, ranging from modest to significant. Some minor adverse effects were reported: mouth sores, eczema, gastrointestinal reaction, dyslipidemia, and neutropenia.ConclusionSirolimus was efficient in children with refractory head and neck LMs and was well tolerated.

Objective: Repeat sclerotherapy of lymphatic malformations (LMs) is challenging. Accordingly, the aim of the present article is to describe a simulated angiography technique-a new method of bleomycin infusion for the treatment of LMs to achieve better outcome(s) in fewer sessions. Materials and Methods: A retrospective analysis of information housed in a prospectively collected LM database was performed. Patients with LM, revealed on imaging examination and treated using a simulated angiography technique with a bleomycin mixture, were included in the study. Visual evaluation and imaging examinations were performed to evaluate clinical improvement. Results: A total of 151 patients (82 male, 69 female; mean age, 28.29 months [range 1 month-12 years]) with LMs were included in this study. Excellent visual and radiological resolution was observed in 77% (117/151) of lesions, 17% (26/151) had significant improvement, and 8 patients exhibited a slight response. The number of procedures per patient varied from 1 to 5, and the average number of treatment sessions for LM was 1.34. Side effects included skin erythema at the injection site, local swelling, mild tenderness, and fever, which were controlled by oral antipyretics. No serious side effects were observed. Conclusions: Simulated angiography using a bleomycin mixture for sclerotherapy of LMs in children was feasible and demonstrated good effect with little trauma and less time per treatment.

Undifferentiated sarcoma (UNDS) of the soft tissue is an exceedingly rare disease. Its diagnosis depends mainly on molecular and immunohistochemical analyses to exclude other soft-tissue sarcomas. It is difficult to confirm a positive diagnosis by imaging pathological features because of their rarity and similarity with other conditions. Since 2013, only 13 cases of undifferentiated soft-tissue sarcoma, mostly diagnosed through imaging of solid tumors in infant and children, have been reported. The authors present a rare case of a 3-month-old Chinese boy with UNDS primarily in the left lower extremity and characterized by a cystic and solid growth pattern.

Introduction: Infantile hepatic hemangioendothelioma is a type of benign hepatic tumor that occurs in infancy. Many hepatic tumors are diagnosed when screening is done for multiple cutaneous hemangiomas. Hepatic tumors are small multifocal lesions and are mostly asymptomatic. There have been many case reports of asymptomatic infantile hepatic hemangioendothelioma, but few of these have pointed out that hepatic hemangiomas can sometimes be life-threatening due to fatal hepatomegaly complications such as pulmonary artery hypertension or even congestive heart failure. At present, there are no standard protocols for treating infantile hepatic hemangioendothelioma, though most clinicians agree that treatment is unnecessary for multiple small hepatic hemangiomas in asymptomatic patients. Little is known about treatment for cases with life-threatening complications induced by infantile hepatic hemangioendothelioma as there are so few reported cases. Here, we report a special case with hepatomegaly, pulmonary artery hypertension, and cardiac insufficiency induced by infantile hepatic hemangioendothelioma. Patient concerns: We present a case with hepatomegaly, pulmonary artery hypertension, and cardiac insufficiency caused by infantile hepatic hemangioendothelioma. Diagnosis: Infantile hepatic hemangioendothelioma was diagnosed. Interventions: The patient underwent transcatheter arterial embolization and was given propranolol. Outcomes: The patient responded well to treatment with transcatheter arterial embolization and propranolol. The patient gained weight steadily, liver volume, and mass size have decreased considerably, echocardiography showed normal pulmonary artery pressure and ejection fraction, and we discovered no serious complications after 1 year of follow-up. Conclusion: Transcatheter arterial embolization combined with propranolol is an effective treatment for life-threatening infantile hepatic hemangioendothelioma.

Propranolol is the mainstay of treatment for infantile hemangiomas (IHs) benefited from its low complication in the present study. However, it has an uncertainty treating period with cumbersome methods which may be related to clinical features. This study sought to considered possible influences of short-term efficacy to medication. Retrospective analysis of 82 patients with IHs treated by propranolol was performed. The patients were grouped according to effect (excellent, good and fair/poor). ANOVA or t test was used to assess the relationships between effect and clinical features of IHs. Twenty-seven patients were males and 55 were females. The median age of treatment initiation was 3.5 (±2.11) months. Mean follow-up time for the group was 6.2 months (1.5–16 months). There were no significant associations between short-term efficacy and gender, time points of treatment, diameter of tumor and multifocality. However, tumor thickness was associated with short-term efficacy (P = .013). Moreover, an obvious difference of short-term efficacy has been found when tumor thickness

This technical note presents strategies for systematic ways to control mobile actuators in distributed wireless sensor and actuator networks. The efficiency, responsiveness, and service lifetime of the mobile actuators depend on the allocation of the workload to each individual actuator. Starting from an initial deployment, the proposed distributed multi-actuator control algorithm computes the load in each partition and uses the load imbalance as a virtual force to dynamically move the actuators until a balanced distribution is achieved. The load in each partition for an individual actuator includes both the total travel cost to visit or contact all sensors in the partition and the cost of servicing them. Finding the minimum workload allocation to serve each partition is an NP-hard problem. Therefore, efficient heuristics are proposed to do the partition, plan the path of each actuator and calculate its workload. Simulation results demonstrate the effectiveness of the proposed strategies.

Network passive monitoring is an important method in debugging and analyzing wireless sensor networks. The passive monitoring method for wireless sensor networks (WSN) has the following problems: monitoring traces are incomplete and contain only limited information because some packets may be misheard; how to infer implicit information about network protocol behavior and how to detect network events from incomplete traces are challenging issues in passive monitoring system. To resolve the above issues, we propose the use of time tuning in trace merging and a finite state machine for inferring missing packets, implicit information and network event detection. (1) In order to enhance the integrity of traces, we propose clock-adjusting strategies including clock fine-tuning and clock self-adjustment to solve clock drifting in trace merging. (2) For inference of missing packets and implicit information, we devise and implement a finite state machine to infer missing packets and packet receiving state for multi-hop network protocols. (3) For network event detection, we propose an XML-based description of event rule and network event visualization methods. These methods are implemented in PMSW (passive monitoring system for WSN). We verify the accuracy of merging and inference modules with monitoring traces in the Collect Tree Protocol sensor network. Experiments show that the finite state machine can infer 40-60% of missing packets and more than 90% of the receiving state of data packets, when the trace has more than 50% of the data packets captured in the network.

Intra-arterial chemotherapy (IAC) has become an essential technique for the management of advanced intraocular retinoblastoma (RB). In this study, the aim of this article is to describe the clinical results and the short-term complications of IAC performed in our hospital. We retrospectively analyzed patients with newly diagnosed unilateral advanced intraocular (group D or E) RB undergoing IAC from October 2016 to December 2017 in our hospital. We recorded the data including age, gender, cycles of IAC, pathway of arteries approached (ophthalmic artery or middle meningeal artery), ocular and systematic complications, globe salvage. Sixty-one patients underwent IAC performing 189 procedures with a median of 3.1 sessions per eye (range, 1–5 sessions). The overall globe salvage rate is 78.7% (Group D (84.2%), and Group E (69.6%) and followed-up. Short-term ocular complications include eyelid edema (15 cases), ptosis (5 cases), forehead congestion (3 cases), retina hemorrhage (5 cases), choroid atrophy (2 cases), phthisis bulbi (1 case), bradycardia and hypotension during the procedure (7cases), myelosuppressions (6 cases), and nausea and vomiting (5cases). IAC is safe and effective for the treatment of unilateral advanced intraocular RB with a very low complication rate.

The FE parametric quadratic programming (PQP) method developed from the parametric variational principle (PVP) was used for the analysis of the stress distribution of the 3D elastoplastic frictional contact of an impeller-shaft sleeve-shaft. A locomotive-type turbocharger compressor with 24 blades under combined centrifugal and interference-fit loading was considered in the numerical analysis. The solution of elastoplastic frictional contact problems belongs to unspecified boundary problems where the interaction between two kinds of nonlinearities should occur. To save time in the numerical computation, a multi-substructure technique was adopted in the structural modeling. The effect of fit tolerance, wall thickness of the shaft sleeve, and rotational speed on the contact stress was discussed in detail in the numerical computation. To decrease the difficulty of the assembly process and ensure the safety of the working state, the amount of interference between the shaft sleeve and shaft by press-fitting should be controlled strictly to avoid the rapid increase of contact stress. The numerical results show that the algorithm has high accuracy and good convergence. The study can be referred to in deciding the proper fit tolerance and improving the design and manufacturing technology of compressor impellers.

This paper studies the retinal vessel radius estimation and proposes a segmentation method for vessel center lines based on ridge descriptors. The study on radius estimation reveals that the radius estimation by the matched filters based on the second order derivatives of Gaussian kernels is only correct at the vessel center. The relation between the vessel radius and the scale of the Gaussian kernel in the estimation method based on the normalized largest curvature is also studied. The ridge descriptor proposed in this paper contains the normalized largest curvature and the orientations of gradients in the local neighborhood. For vessels of a certain scale, the distribution of the descriptors is assumed to be a normal distribution and is learned from a training set with known truth. Vessel center line segmentation can be then performed based on the distance between the ridge descriptor at candidate pixels and the learned model. Evaluation of the vessel center line segmentation based on the descriptors is done on both DRIVE and STARE databases using the receiver operating characteristic (ROC) curves. The areas under the ROC curves on DRIVE and STARE databases are 0.9584 and 0.9421 respectively.

Epidemiological studies suggest that obesity is a risk of thyroid cancer, especially papillary thyroid cancer (PTC). However, the associations of obesity and clinic–pathological features, especially the association of body mass index (BMI) and lymph node metastasis of thyroid cancer are unclear. Seven hundred ninety-six primary patients with PTC were enrolled in this retrospective cohort study. The relationships between BMI and clinic-pathological features of PTC were evaluated by logistic regression models based on the 5-point increase in BMI and BMI quartiles (underweight, normal weight, overweight, and obesity). The 5-point increase in BMI was strongly associated with extra-thyroidal invasion [odds ratio (OR) 2.201, P

In this paper, we propose a Bayesian probability based framework, which uses visual keywords and already available text keywords to automatically annotate the images. Taking the cue from document classification, an image can be considered as a document and objects present in it as words. Using this concept, we can create visual keywords by dividing an image into tiles based on a certain template size. Visual keywords are simple vector quantization of small-sized image tiles. We estimate the conditional probability of a text keyword in the presence of visual keywords, described by a multivariate Gaussian distribution. We demonstrate the effectiveness of our approach by comparing predicted text annotations with manual annotations and analyze the effect of text annotation length on the performance.

The accurate localization of sensor nodes in a mobile robotic sensor network is important in many surveillance applications. Based on our previous work in sensor localization, this paper introduces a mobility assisted localization approach guided by network rigidity for mobile robotic sensor networks. The rigidity test of a network is based on a pebble game algorithm, that decides whether the network is uniquely localizable or not. If its local network is not uniquely localizable, the mobile node circles around its original position to collect at least two extra ranging data with each of its neighbors while recording its trajectory using odometer. We prove that the extra data with robot trajectory will make the local network uniquely localizable. The local maps of all the nodes are then merged into a globally consistent map. We evaluate the effectiveness of the proposed approach using two examples of low-degree networks and four types of topologically different networks.

This paper presents strategies for systematic ways to deploy multiple mobile robots for servicing large numbers of points of interest in a distributed fashion. The mobile robots in such systems are responsible for providing several essential services. For example, in manufacturing automation, the tasks could include parts inspection, parts changing and data collection, etc. The efficiency, responsiveness, and service life of the mobile robots depend on the balanced allocation of the entire work load to the individual robots. Starting from an initial random deployment of the robots, the proposed distributed load balancing algorithm computes the load share of each robot using virtual potential force method. The load imbalance is used as the virtual force to dynamically move the robots until a more balanced distribution is achieved. The total travelling cost to visit all the points of interest in a partition and the cost of servicing individual points are combined to compute the load in each partition. To verify the effectiveness of the proposed algorithms, we conducted simulations by applying them in inspection applications and obtained satisfactory results.

In this paper, we propose an approach to negotiate the gap between low-level image features and the human interpretation of the image. Taking the cue from text-based retrieval techniques, we construct "visual keywords" using vector quantization of small- sized image tiles. Both visual and textual keywords are combined and used to represent an image as a single multimodal vector. We use a diffusion kernel-based non-linear approach to fuse the visual and textual keywords. By comparing the performance of this approach with a low-level features-based approach, we demonstrate that visual keywords, when combined with textual keywords, improve the image retrieval results significantly.

We present a general framework for vessel segmentation in retinal images with a particular focus on small vessels. The retinal images are first processed by a nonlinear diffusion filter to smooth vessels along their principal direction. The vessels are then enhanced using a compound vessel enhancement filter that combines the eigenvalues of the Hessian matrix, the response of matched filters, and edge constraints on multiple scales. The eigenvectors of the Hessian matrix provide the orientation of vessels and so only one matched filter is necessary at each pixel on a given scale. This makes the enhancement filter is more efficient compared with existing multiscale matched filters. Edge constraints are used to suppress the response of spurious boundary edges. Finally, the center lines of vessels are tracked from seeds obtained using multiple thresholds of the enhanced image. Evaluation of the enhancement filter and the segmentation is performed on the publicly available DRIVE database.

Wireless sensor networks have been widely used in many applications such as environment monitoring, surveillance systems and unmanned space explorations. However, poor deployment of sensor devices leads (1) bad network connectivity which makes data communication or data collection very hard; or (2) redundancy of coverage which wastes energy of sensors and causes redundant data in the network. Thus, in this paper, we propose using a mobile robot to assist the sensor deployment and data collection for unmanned explorations or monitoring. We assume that the robot can carry and deploy the sensor devices, and also have certain communication capacity to collect the data from the sensor devices. Given a set of interest points in an area, we study the following interesting problems: (1) how to decide minimum number of sensor devices to cover all the interest points; (2) how to schedule the robot to place these sensor devices in certain position so that the path of the robot is minimum; and (3) after the deployment of sensors, how to schedule the robot to visit and communicate with these sensor devices to collect data so that the path of the robot is minimum. We propose a complete set of heuristics for all these problems and verify the performances via simulation.

In classical image classification approaches, low-level features have been used. But the high dimensionality of feature spaces poses a challenge in terms of feature selection and distance measurement during the clustering process. In this paper, we propose an approach to generate visual keyword and combine both visual and text keywords of the image to form a multimodal vector for image classification. This multimodality helps in extracting the image to image, text to text and text to image relations. A visual keyword is derived using vector quantization of image tiles. We arrange the visual keywords in a manner analogous to the term-document matrix in information retrieval. The visual keywords when combined with text keywords result in improvement in the quality of classification. We use a recently proposed nonlinear dimensionality reduction technique, diffusion maps, to reduce the dimensionality of the image representation. Our method is evaluated on two public datasets: LabelMe and Corel. The results support the conclusion that the proposed method of combining visual and text keywords is robust and produces good quality clusters.

In this paper, we define a mobile self-localization (MSL) problem for sparse mobile sensor networks, and propose an algorithm named mobility assisted MDS-MAP(P), based on multi-dimensional scaling (MDS) for solving the problem. For sparse sensor networks, all the existing localization algorithms fail to work properly due to the lack of distance or connectivity data to uniquely calculate the geo-locations. In MSL, we use mobile sensors to add extra distance constraints to a sparse network, by moving the mobile sensors in the area of deployment and recording distances to neighbors at some intermediate locations. MSL can also be used for localizing and tracking mobile objects in a robotic or body sensor network. Experiments and evaluations of the new algorithm are provided.

Optic fundus assessment is widely used for diagnosing vascular and non-vascular pathology. Inspection of the retinal vasculature may reveal hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. Due to various imaging conditions retinal images may be degraded. Consequently, the enhancement of such images and vessels in them is an important task with direct clinical applications. We propose a novel technique for vessel enhancement in retinal images that is capable of enhancing vessel junctions in addition to linear vessel segments. This is an extension of vessel filters we have previously developed for vessel enhancement in thoracic CT scans. The proposed approach is based on probabilistic models which can discern vessels and junctions. Evaluation shows the proposed filter is better than several known techniques and is comparable to the state of the art when evaluated on a standard dataset. A ridge-based vessel tracking process is applied on the enhanced image to demonstrate the effectiveness of the enhancement filter.

We propose a novel vessel enhancement filter for retinal images. The filter can be used as a preprocessing step in applications such as vessel segmentation/visualization, and pathology detection. The proposed filter combines the eigenvalues of the Hessian matrix, the response of matched filters, and edge constraints on multiple scales. The eigenvectors of the Hessian matrix provide the orientation of vessels and so only one matched filter is necessary at each pixel in a given scale. This makes the proposed filter more efficient compared with existing multiscale matched filters. Edge constraints are used to suppress the response of spurious boundary edges. Experimental evaluation on the publicly available DRIVE dataset demonstrate improved performance of the proposed filter compared with known techniques.

In the clustering of large number of images using low-level features, one of the problems encountered is the high dimensional feature space. The high dimensionality of feature spaces leads to unnecessary cost in feature selection and also in the distance measurement during the clustering process. In this paper, we propose an approach to reduce the dimensionality of the feature space based on diffusion maps. In the proposed approach, each image is represented by a set of tiles. A visual keyword-image matrix is derived from classifying these tiles into a set of clusters and counting the occurrence of each cluster in each image of our database. The visual keyword-image matrix is similar to the term-document matrix in information retrieval. We use diffusion maps to reduce the dimensionality of visual keyword matrix. By reducing the dimensionality of the image representation, we can save computation cost significantly. We compare the performance between the proposed approach and the approach that uses the global MPEG-7 color descriptors. The results demonstrate the improvements.

Volume registration is fundamental to multiple medical imaging algorithms. Specifically, non-rigid registration of thoracic CT scans taken at different time instances can be used to detect new nodules more reliably and assess the growth rate of existing nodules. Voxel-based registration techniques are generally sensitive to intensity variation and structural differences, which are common in CT scans due to partial volume effects and naturally occurring motion and deformations. The approach we propose in this paper is based on vessel tree extraction which is then used to infer the complete volume registration. Vessels form unique features with good localization. Using extracted vessel trees, a minimization process is used to estimate the motion vectors at vessels. Accurate motion vectors are obtained at vessel junctions whereas vessel segments support only normal component estimation. The obtained motion vectors are then interpolated to produce a dense motion field using thin plate splines. The proposed approach is evaluated on both real and synthetically deformed volumes. The obtained results are compared to several standard registration techniques. It is shown that by using vessel structure, the proposed approach results in improved performance.

Vessel enhancement in volumetric data is a necessary prerequisite in various medical imaging applications. In the context of automated lung nodule detection in thoracic CT scans, segmented blood vessels can be used to resolve local ambiguities based on global considerations and so improve the performance of lung nodule detection algorithms. Segmenting the data correctly is a difficult problem with direct consequences for subsequent processing steps. Voxels belonging to vessels and nodules in thoracic CT scans are both characterized by high contrast with respect to a local neighborhood. Thus in order to enhance vessels while suppressing nodules, additional characteristics should be used. In this paper we propose a novel vessel enhancement filter that is capable of enhancing vessels and junctions in thoracic CT scans while suppressing nodules. The proposed filters are based on a Gaussian mixture model which is optimized through expectation maximization. The proposed filters are based on first order differential quantities and so are less sensitive to noise compared with known Hessian-based vessel enhancement filters. Moreover, the proposed filters utilize an adaptive window and so avoid the common need for multiple scale analysis. The proposed filters are evaluated and compared to known techniques qualitatively and quantitatively on both synthetic and actual clinical data and it is shown that the proposed filters perform better.

Vessel tree reconstruction in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automated lung nodule detection in thoracic computed tomography (CT) scans, vessel trees can be used to resolve local ambiguities based on global considerations and so improve the performance of nodule detection algorithms. In this study, a novel approach to vessel tree reconstruction and its application to nodule detection in thoracic CT scans was developed by using correlation-based enhancement filters and a fuzzy shape representation of the data. The proposed correlation-based enhancement filters depend on first-order partial derivatives and so are less sensitive to noise compared with Hessian-based filters. Additionally, multiple sets of eigenvalues are used so that a distinction between nodules and vessel junctions becomes possible. The proposed fuzzy shape representation is based on regulated morphological operations that are less sensitive to noise. Consequently, the vessel tree reconstruction algorithm can accommodate vessel bifurcation and discontinuities. A quantitative performance evaluation of the enhancement filters and of the vessel tree reconstruction algorithm was performed. Moreover, the proposed vessel tree reconstruction algorithm reduced the number of false positives generated by an existing nodule detection algorithm by 38%.

Blood vessel segmentation in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automatic lung nodule detection in thoracic CT scans, segmented blood vessels can be used in order to resolve local ambiguities based on global considerations and so improve the performance of lung nodule detection algorithms. In this paper, a novel regulated morphology approach to fuzzy shape analysis is described in the context of blood vessel extraction in thoracic CT scans. The fuzzy shape representation is obtained by using regulated morphological operations. Such a representation is necessary due to noise present in the data and due to the discrete nature of the volumetric data produced by CT scans, and particularly the interslice spacing. Regulated morphological operations are a generalization of ordinary morphological operations which relax the extreme strictness inherent to ordinary morphological operations. Based on constraints of collinearity, size, and global direction, a tracking algorithm produces a set of connected trees representing blood vessels and nodules in the volume. The produced tree structures are composed of fuzzy spheres in which the degree of object membership is proportional to the ratio between the occupied volume and the volume of the discrete sphere encompassing it. The performance of the blood vessel extraction algorithm described in the paper is evaluated based on a distance measure between a known blood vessel structure and a recovered one. As the generation of synthetic data for which the true vessel network is known may not be sufficiently realistic, our evaluation is based on different versions of real data corrupted by multiplicative Gaussian noise.

Using the finite element parametric quadratic programming method, computation of Elastic and Elastic-plastic rolling contact problems between wheel and rail is carried out for various cases such as different wheel diameters, different axle loads, different tractive force and different friction factors. The contact states and the contact internal force between wheel and rail are obtained, and their changing laws corresponding with every above-mentioned parameter are analyzed in this paper.Copyright © 2002 by ASME

Document analysis and graphics recognition algorithms are normally applied to the processing of images of 2D documents scanned when flattened against a planar surface. Technological advancements in recent years have led to a situation in which digital cameras with high resolution are widely available. Consequently, traditional graphics recognition tasks may be updated to accommodate document images captured through a hand-held camera in an uncontrolled environment. In this paper the problem of perspective and geometric deformations correction in document images is discussed. The proposed approach uses the texture of a document image so as to infer the document structure distortion. A two-pass image warping algorithm is then used to correct the images. In addition to being language independent, the proposed approach may handle document images that include multiple fonts, math notations, and graphics. The de-warped images contain less distortions and so are better suited for existing text/graphics recognition techniques.

Document analysis and graphics recognition algorithms are normally applied to the processing of images of 2D documents scanned when flattened against a planar surface. Technological advancements in recent years have led to a situation in which digital cameras with high resolution are widely available. Consequently, traditional graphics recognition tasks may be updated to accommodate document images captured through a camera in an uncontrolled environment. In this paper the problem of document image rectification is discussed. The rectification targets the correction of perspective and geometric distortions of document images taken from uncalibrated cameras, by synthesizing new views which are better suited for existing graphics recognition and document analysis techniques. The proposed approach targets cases in which the document is not necessarily flat, without relaying on specific modeling assumptions, and by utilizing one or more overlapping views of the document. Document image rectification results are provided for several cases.

This paper introduces a rigidity-guided localisation approach for mobile robotic sensor networks. The localisation uses a distance graph composed of both the robot-to-robot ranging data and the motion trajectories from robot odometry. The motion of a robot depends on the result of the rigidity test of its local distance graph: if the graph is not uniquely localisable, the robot moves around in its neighbourhood to collect at least two extra ranging data with each of its neighbours in order to make the extended graph uniquely localisable. Locally unique maps are then merged into a globally consistent map.

This paper addresses the scatternet formation for large scale multi-hop Bluetooth networks. We first describe an efficient method to build a Connected Dominating Set (CDS) as the backbone of multi-hop Bluetooth network, then propose new algorithms to form the dBBlue scatternets (Song et al., 2005) in each cluster. The final scatternet, M-dBBlue, guarantees the connectivity. Our experiment shows our scatternet seldom parks any node. We then propose a complete set of hierarchical routing methods for M-dBBlue which enables the self-routing inside each cluster. Moreover, our scatternet formation and routing algorithm do not necessarily require position information of the node.

The Chinese primary cesium beam frequendy standard CSIII has been reported at the previous Symposium [1]. This paper describes briefly its major improvements and its reevaluation by the Time/Frequency Division, NIM in the recent years.