Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected works), Vol. 2

This second volume dedicated to Dezert-Smarandache Theory (DSmT) in Information Fusion brings in new fusion quantitative rules (such as the PCR1-6, where PCR5 for two sources does the most mathematically exact redistribution of conflicting masses to the non-empty sets in the fusion literature), qualitative fusion rules, and the Belief Conditioning Rule (BCR) which is different from the classical conditioning rule used by the fusion community working with the Mathematical Theory of Evidence. Other fusion rules are constructed based on T-norm and T-conorm (hence using fuzzy logic and fuzzy set in information fusion), or more general fusion rules based on N-norm and N-conorm (hence using neutrosophic logic and neutrosophic set in information fusion), and an attempt to unify the fusion rules and fusion theories. The known fusion rules are extended from the power set to the hyper-power set and comparison between rules are made on many examples. One defines the degree of intersection of two sets, degree of union of two sets, and degree of inclusion of two sets which all help in improving the all existing fusion rules as well as the credibility, plausibility, and communality functions. The book chapters are written by Frederic Dambreville, Milan Daniel, Jean Dezert, Pascal Djiknavorian, Dominic Grenier, Xinhan Huang, Pavlina Dimitrova Konstantinova, Xinde Li, Arnaud Martin, Christophe Osswald, Andrew Schumann, Tzvetan Atanasov Semerdjiev, Florentin Smarandache, Albena Tchamova, and Min Wang

Local bone metabolism during the consolidation process of spinal interbody fusion

INTRODUCTION: Although computed tomography (CT) can identify the presence of eventual bony bridges following lumbar interbody fusion (LIF) surgery, it does not provide information on the ongoing formation process of new bony structures. 18F sodium fluoride (18F-NaF) positron emission tomography (PET) could be used as complementary modality to add information on the bone metabolism at the fusion site. However, it remains unknown how bone metabolism in the operated segment changes early after surgery in uncompromised situations. This study aimed to quantify the changes in local bone metabolism during consolidation of LIF. MATERIALS AND METHODS: Six skeletally mature sheep underwent LIF surgery. 18F-NaF PET/CT scanning was performed 6 and 12 weeks postoperatively to quantify the bone volume and metabolism in the operated segment. Bone metabolism was expressed as a function of bone volume. RESULTS: Early in the fusion process, bone metabolism was increased at the endplates of the operated vertebrae. In a next phase, bone metabolism increased in the center of the interbody region, peaked, and declined to an equilibrium state. During the entire postoperative time period of 12 weeks, bone metabolism in the interbody region was higher than that of a reference site in the spinal column. CONCLUSION: Following LIF surgery, there is a rapid increase in bone metabolism at the vertebral endplates that develops towards the center of the interbody region. Knowing the local bone metabolism during uncompromised consolidation of spinal interbody fusion might enable identification of impaired bone formation early after LIF surgery using 18F-NaF PET/CT scanning

ILNet: Low-level Matters for Salient Infrared Small Target Detection

Infrared small target detection is a technique for finding small targets from infrared clutter background. Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN's representation ability. To address the above problem, in this paper, we propose an infrared low-level network (ILNet) that considers infrared small targets as salient areas with little semantic information. Unlike other SOTA methods, ILNet pays greater attention to low-level information instead of treating them equally. A new lightweight feature fusion module, named Interactive Polarized Orthogonal Fusion module (IPOF), is proposed, which integrates more important low-level features from the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation of low dimensional information according to the number of input channels. In addition, the idea of ensemble learning is used to design a Representative Block (RB) to dynamically allocate weights for shallow and deep layers. Experimental results on the challenging NUAA-SIRST (78.22% nIoU and 1.33e-6 Fa) and IRSTD-1K (68.91% nIoU and 3.23e-6 Fa) dataset demonstrate that the proposed ILNet can get better performances than other SOTA methods. Moreover, ILNet can obtain a greater improvement with the increasement of data volume. Training code are available at https://github.com/Li-Haoqing/ILNet

An Image Registration Method for Head CTA and MRA Images Using Mutual Information on Volumes of Interest

Image registration is an important and a fundamental task in computer vision and image processing field. For example, to make a surgical plan for head operation, the surgeons should gain more detailed information from CT angiography (CTA) and MR angiography (MRA) images. And the abnormalities can be easily detected from the fusion image which is obtained from two different modalities. One of the multiple modal image registration methods is matching the CTA and MRA, by which the image of head vascular could be enhanced. In general, the procedure for fusion is completed manually. It is time-consuming and subjective. Particularly the anatomical knowledge is required as well. Therefore, the development of automatic registration methods is expected in medical fields. In this paper, we propose a method for high accurate registration, which concentrates the structure of head vascular. We use 2-D projection images and restrict volume of interests to improve the processing affection. In experiments, we performed our proposed method for registration on five sets of CTA and MRA images and a better result from our previous method is obtained.SCIS&ISIS 2014 : Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent, December 3-6, 2014, Kitakyushu, Japa

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.Comment: This article will be presented at MICCAI (Medical Image Computing and Computer-Assisted Intervention) 201

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders’ requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9)

Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction

State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors usually reduce drift in camera tracking by globally optimizing the estimated camera poses in real-time without simultaneously updating the reconstructed surface on pose changes. We propose an efficient on-the-fly surface correction method for globally consistent dense 3D reconstruction of large-scale scenes. Our approach uses a dense Visual RGB-D SLAM system that estimates the camera motion in real-time on a CPU and refines it in a global pose graph optimization. Consecutive RGB-D frames are locally fused into keyframes, which are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a novel keyframe re-integration strategy with reduced GPU-host streaming. We demonstrate in an extensive quantitative evaluation that our method is up to 93% more runtime efficient compared to the state-of-the-art and requires significantly less memory, with only negligible loss of surface quality. Overall, our system requires only a single GPU and allows for real-time surface correction of large environments.Comment: British Machine Vision Conference (BMVC), London, September 201

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure