nils-herrmann/clean_scirepeval huggingface dataset:
Towards a diffusion image processing validation and accuracy prediction framework

Validation is the main bottleneck preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some objective metrics. In this work, a different approach based on Petri Nets (PN) is proposed. The basic idea consists in predicting the accuracy that will result from a given processing based on the characterization of the sources of inaccuracy of the system. Here we propose a proof of concept in the scenario of a diffusion imaging analysis pipeline. A PN is built after the detection of the possible sources of inaccuracy. By integrating the first qualitative insights based on the PN with quantitative measures, it is possible to optimize the PN itself, to predict the inaccuracy of the system in a different setting. Results show that the proposed model provides a good prediction performance and suggests the optimal processing approach.
Infinite feature selection on shore-based biomarkers reveals connectivity modulation after stroke

Connectomics is gaining increasing interest in the scientific and clinical communities. It consists in deriving models of structural or functional brain connections based on some local measures. Here we focus on structural connectivity as detected by diffusion MRI. Connectivity matrices are derived from microstructural indices obtained by the 3D-SHORE. Typically, graphs are derived from connectivity matrices and used for inferring node properties that allow identifying those nodes that play a prominent role in the network. This information can then be used to detect network modulations induced by diseases. In this paper we take a complementary approach and focus on link as opposed to node properties. We hypothesize that network modulation can be better described by measuring the connectivity alteration directly in the form of modulation of the properties of white matter fiber bundles constituting the network communication backbone. The goal of this paper is to detect the paths that are most altered by the pathology by exploiting a feature selection paradigm. Temporal changes on connection weights are treated as features and those playing a leading role in a patient versus healthy controls classification task are detected by the Infinite Feature Selection (Inf-FS) method. Results show that connection paths with high discriminative power can be identified that are shared by the considered microstructural descriptors allowing a classification accuracy ranging between 83% and 89%.
Neurophysiological measures for users' training objective assessment during simulated robot-assisted laparoscopic surgery.

Minimally invasive surgery can be performed with robotic assistance, as evolution of laparoscopic surgery. Robots for assisted surgery are far from being user friendly and require extensive training. To this end, ad-hoc devices and experimental set-ups are needed. The da Vinci system is one of the most diffused surgical robotics technology. The aim of the study was two-fold: i) to propose a neurophysiological measure by which objectively assess the learning progress of the users by means of a simulator of the da Vinci system, and ii) to demonstrate the advantages of cognitive assessment with respect to the standard methodologies for the evaluation of training efficiency.
Semantic segmentation of angiographic images

The overall purpose of this work is to identify objects in an angiographic sequence by exploiting the temporal correlation between adjacent frames for analysis and compression purposes. The detection of the vascular tree in a reference image can support segmentation in adjacent frames by reducing the detection problem to a tracking procedure along the sequence. Object identification also allows an object-oriented approach to compression, as suggested by medical images nature. A vessel segmentation algorithm has been designed and implemented; it is an extrapolation and update procedure which exploits the a priori knowledge about the image content.
DWT based non-parametric texture modeling

We propose a non-parametric texture modeling and synthesis technique based on the integer version of the discrete wavelet transform (DWT). The successive levels of the DWT pyramid of the input texture are progressively sampled starting from the signal approximation to generate the analogous wavelet pyramid for the synthetic texture. An underlying statistical model is assumed, where the appearance of wavelet coefficients at each scale is conditioned by the appearance of the corresponding ancestors at coarser scales. A non-parametric Parzen estimator is used for sampling. The integer DWT is obtained by the lifting steps implementation. The proposed method provides results comparable to the other state-of-the-art techniques for random (unstructured) textures, but at a very low computational complexity. For structured textures, performance depends on the specific orientation features and structure size.
Towards a semantic-driven metric for image quality

We propose a pilot study in view of the definition of a semantic based absolute metric for automatic image quality evaluation. The basic idea is that the behavioral relevance of the objects present in the scene determines the extent of the impact of a given amount of (measurable) degradation on the perceived quality. Such an assumption is investigated following the top-down approach. The images are considered as collections of semantic units holding different relevance in the scene interpretation process. The test set is built such that some pre-defined categories of objects are present and the goal is to determine the impact of the selective degradation of instances of objects pertaining to the different categories on the perceived quality of the corresponding images. In particular, we focus on human faces. Under the assumption that human faces are salient cues from a perceptual point of view in an image quality evaluation task, we investigate the impact of the selective degradation of face/not-face regions of a pre-defined set of natural images through a subjective test performed by human observers. Our hypothesis is that the perceived quality is ruled by the nesses evaluated on human faces.
Multimodal MRI-based tissue classification in breast ductal carcinoma

In this paper we propose a pipeline to integrate breast diffusion and perfusion MRI for diagnosis, surgical planning and follow-up. Dynamic contrast enhanced (DCE) and diffusion weighted (DWI) MRI provide complementary information on the tissue structure and properties: while DCE-MRI allows the characterization of the lesion angiogenesis, DWI techniques can probe the apparent diffusion coefficient (ADC) and therefore assess the nature and cellularity of the lesions. Here we propose a two-step process for the integration of these modalities. First, dissimilarity-based clustering is performed on DCE-MRI to identify the different tumoral subregions. These are then mapped onto the DWI images following inter-modal registration. The probability density functions (PDFs) of the so-identified subregions in the ADC map are extracted and compared through non-parametric testing. Results show that subregions corresponding to different clusters hold statistically different PDFs, indicating a degree of consistency in the information obtained from the two modalities while providing a posterior validation of the registration method. This enables the efficient integration of the information brought by DCE and DWI, respectively, while taking advantage of their complementarity.
Shore-based biomarkers allow patient versus control classification in stroke

In diffusion MRI, numerical biomarkers are usually calculated for research and clinical purposes as Generalized Fractional Anisotropy (GFA). Recently, more eloquent indices allowing a more accurate description of tissue microstructure were derived from the SHORE model. Under certain experimental conditions, such indices express the morphological properties of the compartments where spins diffuse. Evidence of the suitability of such indices as biomarkers for stroke was provided in a previous study based on diffusion spectrum imaging (DSI) and focusing on the cortical motor loop. The goal of this work was to investigate the suitability of such indices for stratification, namely for distinguishing pathological from healthy subjects. To this end, two different paths were followed. First, the same approach used in the previous work for longitudinal analysis (statistics-based) was applied to detect inter-group variations. Then, a new approach based on the LASSO regressor was proposed. Results provided evidence of the suitability of the proposed indices for stratification purposes.
Pansharpening and image quality interface

Pansharpening techniques increase the spatial resolution of multispectral images and also simultaneously preserve the spectral information in the multispectral data. Pansharpening is also expressed as resolution merge, image integration, and multisensor data fusion. Some of the applications of pansharpening are to improve geometric correction, enhance certain features that are not visible in the data, change detection using temporal data sets, and enhance classification. There is an increasing use of these different pansharpening methods as a preprocessing step for feature enhancement and improved classification accuracy [Pohl and Van Genderen, 1998]. The quality of the sharpened images is to be evaluated using quantitative metrics. Accordingly, a user-friendly pansharpening and image quality interface (PSIQI) is developed to benchmark the performance of different pansharpening algorithms.
Hyperspectral data analysis using wavelet-based classifiers

In general, the analysis of hyperspectral remote sensing data by means of pattern recognition and/or classification is known to be data dependent. Thus, conventional methods for classifications may not be applicable due to the large amount of data collection used to characterize hyperspectral data in terms of optimality and computational time. In this paper, wavelet-based classifiers are presented and hyperspectral signatures are extracted from the available data and then used for the discrimination of various sample types of vegetation.
Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

Recent developments in sensor technology are contributing toward the tremendous growth of remote sensing (RS) archives (currently, at the petabyte scale). However, this data largely remain unexploited due to the current limitations in the data discovery, querying, and retrieval capabilities. This issue becomes exacerbated in disaster situations, where there is a need for rapid processing and retrieval of the affected areas. Furthermore, the retrieval of images based on the spatial configurations of affected regions [land use/cover (LULC) classes] in an image is important in disaster situations such as floods and earthquakes. The majority of existing Earth observation (EO) image information mining (IIM) systems does not consider the spatial relations among image regions during image retrieval (aka spatial semantic gap). In this work, we have specifically addressed two issues, i.e., explicit modeling of topological and directional relationships between image regions, and development of a resource description framework (RDF)-based spatial semantic graphs (SSGs). This enables more intuitive querying and reasoning on the archived data. A spatial IIM (SIIM) framework is proposed, which integrates a logic-based reasoning mechanism to extract the hidden spatial relationships (both topological and directional) and enables image retrieval based on spatial relationships. The system is tested using several spatial relations-based queries on the RS image repository of flood-affected areas to check its applicability in post flood scenario. Precision, recall, and F-measure metrics were used to evaluate the performance of the SIIM system, which showed good potential for spatial relations-based image retrieval.
Detection and classification of buried radioactive-metal objects using wideband EMI data

Gamma-ray spectroscopy is frequently used for the detection of radioactive materials. As an alternative, we explore the use of electromagnetic induction (EMI) data for detection and classification of radioactive-metal objects, i.e., depleted uranium (DU), in this study. To reduce false alarms, a pattern recognition approach based on a decision tree structure is proposed. In an initial experiment, the DU rounds were placed in rows at three different depths in a rectangular field and EMI measurements are taken. The DU objects placed up to depth 30 cm below surface were successfully detected and identified along with the depth information. The algorithm also outperformed traditional threshold detection based method in terms of discriminating objects at 30cm depth.
Hyperspectral Pixel Unmixing via Spectral Band Selection and DC-Insensitive Singular Value Decomposition

This letter proposes a linear two-class hyperspectral pixel-unmixing algorithm that uses a band selection method to determine the best bands for pixel unmixing, low-pass prefiltering to remove high-frequency content, and a new version of the well-known singular value decomposition (SVD) method, which is insensitive to dc offsets (DCI-SVD). The proposed method is compared to the best level discrete wavelet transform approach for dimensionality reduction and least squares estimation and quadratic programming for unmixing (DWT-LSE-QP). The contributions of this letter are given as follows: (1) the band selection and filter selection algorithm and (2) the DCI-SVD algorithm. The dc insensitivity of the DCI-SVD method is proven, and simulation results using data from an analytical spectral device spectroradiometer show the efficacy of the proposed method and its superiority to the DWT-LSE-QP-based approach in the harder unmixing cases.
Classification performance of random-projection-based dimensionality reduction of hyperspectral imagery

High-dimensional data such as hyperspectral imagery is traditionally acquired in full dimensionality before being reduced in dimension prior to processing. Conventional dimensionality reduction on-board remote devices is often prohibitive due to limited computational resources; on the other hand, integrating random projections directly into signal acquisition offers alternative dimensionality reduction without sender-side computational cost. Effective receiver-side reconstruction from such random projections has been demonstrated previously using compressive-projection principal component analysis (CPPCA). While this prior work has focused on squared-error quality measures, the present work reports experimental results illustrating preservation of statistical class separation and anomaly-detection performance for CPPCA reconstruction following random-projection-based dimensionality reduction.
An ontology merging tool to facilitate interoperability between coastalsensor networks

Ontologies are widely used as a means for solving the information heterogeneity problems on the web because of their capability to provide explicit meaning to the information. In recent years, the growing need to resolve the ambiguities and integrate vocabularies between heterogeneous systems within a domain of interest led to the rapid development of ontologies by different organizations. These ontologies designed for a particular task could be a unique representation of their project needs. Hence, there arises a need to align heterogeneous ontologies to facilitate meaningful knowledge interchange between various sources. Thus, ontology mapping has become the key point to enable semantic interoperability between different representations within a domain. This paper proposes a new instance-based algorithm to automate ontology mapping in ocean sensor networks, whose data are highly heterogeneous in syntax, structure and semantics.
Information Services and Middleware for the Coastal Sensor Web

It is well recognized that semantic conflicts are responsible for the most serious data heterogeneity problems hindering the efficient interoperability between heterogeneous information sources. In recent years, ontologies are widely used as a means for solving the information heterogeneity problems because of their capability to provide explicit meaning to the information. Several organizations are undertaking the development of domain specific ontlolgies to resolve the semantic ambiguities between various domain specific representations. These ontologies designed for a particular task could be a unique representation of their project needs. Hence, there arises a need to align heterogeneous ontologies to facilitate meaningful knowledge interchange between various sources. Thus, ontology mapping has emerged as an important requirement to enable semantic interoperability between different representations within a domain. In this paper we focus on the semantic heterogeneities present in the coastal information sources whose data are highly heterogeneous in syntax, structure and semantics. Ontological modeling was carried out for the various information sources. A data mining approach was adopted to align the concepts belonging to various land cover ontologies. We present a set of standardized information services and middleware for seamless access to information from various networks.
Wrapper-Based Feature Subset Selection for Rapid Image Information Mining

In a disaster, there is a need for rapid image-information retrieval in real or near real time from vast amounts of data coming from multiple remote-sensing sensors. In general, image information mining (IIM) approaches produce enormous amounts of features that are computationally expensive and inefficient to process before the actual information discovery takes place. Also, it is complicated because the combination of the features has little relevance to the hypothesis space. Hence, selecting a relevant subset of features is necessary to overcome these problems and to provide an efficient representation of the target class. In this letter, we propose feature selection and feature transformations based on a wrapper-based genetic algorithm approach. A support vector machine classification is applied for generating predictive models for those land-cover classes that are important in a coastal disaster event. The proposed system, rapid IIM, is a region-based approach where, in lieu of the prevalent pixel-based methods, it localizes interesting zones and enables rapid querying. Results from this study indicate that selecting relevant feature subsets increases the rate of correctly identifying a semantic class and also enables this process with less number of features.
On the use of the extended least squares with simultaneous filtering to identify time-varying ARMAX model

A novel method, based on the combination of weighted extended least squares (WELS) and explicit filtering, for tracking the time-varying parameters of a linear stochastic system is presented. The properties and tracking capability of WELS combined with filtering are analyzed for time-varying linear stochastic system. It is shown that the proposed algorithm has exactly the same tracking capability as WELS if the weighting sequence is appropriately defined. The main advantages of the algorithm are that it improves the parameter estimates and provides the flexibility of using the combined identification scheme and adaptive filtering to track the time-varying parameters.<<ETX>>
Extracting digital terrain models in forestry using lidar data

Airborne light detection and ranging (LIDAR) is emerging as a tool to provide an accurate digital terrain model (DTM) of forest areas since it can even penetrate beneath the canopy. However, the determination of DTM in dense forest areas is still a difficult task and in an early stage of development. In this paper, an adaptive prediction technique based on the least mean squares (LMS) algorithm is presented. Results for LIDAR data, taken in 1999 at the Bellingham, WV site, are considered to illustrate the applicability of the presented technique.
Multipath interference detector design for digital data transmission

In this paper, the effects of multipath transmission interference on a digital communication system is investigated. The channel model for multipath transmission consists of a direct transmission path from the transmitter to the receiver and subchannels corresponding to signals being reflected from the ground plane are included. The output of these subchannels are then recombined in a proper fashion to achieve a better performance. The received signal is modeled as the sum of signals (direct and reflected) having a fading amplitude, an incoherent phase, an attenuation, and a delay due to longer transmission paths. The number of subpaths are optimized to provide maximum improvement, i.e., the detector design is based on the Bayes criterion with a minimum probability of error for a given signal-to-noise ratio. Finally, the detector performance is evaluated in terms of the receiver operating characteristics curves for different signal-to-noise ratio levels.
Radar signal detection via ARMA modeling

A phase shift keyed (PSK) spread spectrum radar signal is analyzed using autoregressive moving average (ARMA) spectral estimation techniques. In general, the ARMA model achieves better PSD estimates and higher resolution than the traditional techniques. Results for simulated spread spectrum radar signals of various lengths in additive white Gaussian noise are presented and a comparison with traditional PSD techniques is made to ascertain the validity of the ARMA model.<<ETX>>
Spectral reduction image processing techniques

Very high-resolution imagery (spatial or spectral) comes at a cost, large files for manipulation and the curse of dimensionality. To overcome these problems, it becomes necessary to reduce the spectral dimensionality of the data with a minimum loss of information. Several techniques will be examined in this paper. These include principal component analysis, singular value decomposition, greedy search, discrete wavelet transform greedy search, and self-organizing maps. The methods are applied to very high spectral resolution data, and results are compared.
Fusion of Radiation and Electromagnetic Induction Data for Buried Radioactive Target Detection and Characterization

In general, buried penetrators made of Depleted Uranium (DU) become hazardous waste. In addition to the detection of DU waste, it is also of interest to know their state of oxidation. However, radioactive target detection techniques usually do not differentiate between metal and oxide. In this study, data fusion techniques are applied to combine results from both the radiation detection and the electromagnetic induction (EMI) detection, so that further differentiation among DU metal, DU oxide, and non-DU metal debris may be achieved. A two-step approach is developed to accomplish decision level fusion. The approach is based on techniques such as majority voting (MV) and weighted majority voting (WMV), in combination with a set of decision rules. The fusion approach has been tested successfully with survey data collected on simulation targets.