nils-herrmann/clean_scirepeval huggingface dataset:
Risk-based requirements management framework with applications to assurance cases

The current regulatory approach for assuring device safety primarily focuses on compliance with prescriptive safety regulations and relevant safety standards. This approach, however, does not always lead to a safe system design even though safety regulations and standards have been met. In the medical device industry, several high profile recalls involving infusion pumps have prompted the regulatory agency to reconsider how device safety should be managed, reviewed and approved. An assurance case has been cited as a promising tool to address this growing concern. Assurance cases have been used in safety-critical systems for some time. Most assurance cases, if not all, in literature today are developed in an ad hoc fashion, independent from risk management and requirement development. An assurance case is a resource-intensive endeavor that requires additional effort and documentation from equipment manufacturers. Without a well-organized requirements infrastructure in place, such “additional effort” can be substantial, to the point where the cost of adoption outweighs the benefit of adoption. In this paper, the authors present a Risk-Based Requirements and Assurance Management (RBRAM) methodology. The RBRAM is an elaborate framework that combines Risk-Based Requirements Management (RBRM) with assurance case methods. Such an integrated framework can help manufacturers leverage an existing risk management to present a comprehensive assurance case with minimal additional effort while providing a supplementary means to reexamine the integrity of the system design in terms of the mission objective. Although the example used is from the medical industry, the authors believe that the RBRAM methodology underlines the fundamental principle of risk management, and offers a simple, yet effective framework applicable to aerospace industry, perhaps, to any industry.
HOSO: Histogram of Surface Orientation for RGB-D Salient Object Detection

Salient object detection using RGB-D data is an emerging field in computer vision. Salient regions are often characterized by an unusual surface orientation profile with respect to the surroundings. To capture such profile, we introduce the histogram of surface orientation (HOSO) feature to measure surface orientation distribution contrast for RGB-D saliency. We propose a new unified model that integrates surface orientation distribution contrast with depth and color contrast across multiple scales. This model is implemented in a multi-stage saliency computation approach that performs contrast estimation using a kernel density estimator (KDE), estimates object positions from the low-level saliency map, and finally refines the estimated object positions with a graph cut based approach. Our method is evaluated on two RGB-D salient object detection databases, achieving superior performance to previous state-of-the-art methods.
A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes

In computed tomography of liver tumors there is often heterogeneous density, weak boundaries, and the liver tumors are surrounded by other abdominal structures with similar densities. These pose limitations to accurate the hepatic tumor segmentation. We propose a level set model incorporating likelihood energy with the edge energy. The minimization of the likelihood energy approximates the density distribution of the target and the multimodal density distribution of the background that can have multiple regions. In the edge energy formulation, our edge detector preserves the ramp associated with the edges for weak boundaries. We compared our approach to the Chan-Vese and the geodesic level set models and the manual segmentation performed by clinical experts. The Chan-Vese model was not successful in segmenting hepatic tumors and our model outperformed the geodesic level set model. Our results on 18 clinical datasets showed that our algorithm had a Jaccard distance error of 14.4 ± 5.3%, relative volume difference of -8.1 ± 2.1%, average surface distance of 2.4 ± 0.8 mm, RMS surface distance of 2.9 ± 0.7 mm, and the maximum surface distance of 7.2 ± 3.1 mm.
Automatic 3D temporal kinetics segmentation of dynamic emission tomography image using adaptive region growing cluster analysis

The primary goal of medical image segmentation is to partition the raw image into region of interests (ROIs) matching the anatomical localization of objects of interest in 2D or 3D space. The traditional method of ROI delineation (or segmentation) for the analysis of dynamic emission tomography is the manual placement of ROIs by the operator. However this approach is operator dependent, time-consuming and may lack good reproducibility. Quantitative positron emission tomography (PET) studies can provide measurements of dynamic physiological and biochemical processes in humans through the use of temporal kinetics available. However, due to the relatively poor spatial resolution and high noise levels, partitioning of ROIs is limited. In this paper, the use of a novel knowledge-based approach to segmentation of clinical PET studies using automatic seed selection for adaptive region growing based on Euclidean distance between the local tissue time-activity curves (TTAC) of the voxels is proposed.
A New Energy Framework With Distribution Descriptors for Image Segmentation

Segmentation of the target object(s) from images that have multiple complicated regions, mixture intensity distributions or are corrupted by noise poses a challenge for the level set models. In addition, the conventional piecewise smooth level set models normally require prior knowledge about the number of image segments. To address these problems, we propose a novel segmentation energy function with two distribution descriptors to model the background and the target. The single background descriptor models the heterogeneous background with multiple regions. Then, the target descriptor takes into account the intensity distribution and incorporates local spatial constraint. Our descriptors, which have more complete distribution information, construct the unique energy function to differentiate the target from the background and are more tolerant of image noise. We compare our approach to three other level set models: 1) the Chan-Vese; 2) the multiphase level set; and 3) the geodesic level set. This comparison using 260 synthetic images with varying levels and types of image noise and medical images with more complicated backgrounds showed that our method outperforms these models for accuracy and immunity to noise. On an additional set of 300 synthetic images, our model is also less sensitive to the contour initialization as well as to different types and levels of noise.
Postreconstruction Nonlocal Means Filtering of Whole-Body PET With an Anatomical Prior

Positron emission tomography (PET) images usually suffer from poor signal-to-noise ratio (SNR) due to the high level of noise and low spatial resolution, which adversely affect its performance for lesion detection and quantification. The complementary information present in high-resolution anatomical images from multi-modality imaging systems could potentially be used to improve the ability to detect and/or quantify lesions. However, previous methods that use anatomical priors usually require matched organ/lesion boundaries. In this study, we investigated the use of anatomical information to suppress noise in PET images while preserving both quantitative accuracy and the amplitude of prominent signals that do not have corresponding boundaries on computerized tomography (CT). The proposed approach was realized through a postreconstruction filter based on the nonlocal means (NLM) filter, which reduces noise by computing the weighted average of voxels based on the similarity measurement between patches of voxels within the image. Anatomical knowledge obtained from CT was incorporated to constrain the similarity measurement within a subset of voxels. In contrast to other methods that use anatomical priors, the actual number of neighboring voxels and weights used for smoothing were determined from a robust measurement on PET images within the subset. Thus, the proposed approach can be robust to signal mismatches between PET and CT. A 3-D search scheme was also investigated for the volumetric PET/CT data. The proposed anatomically guided median nonlocal means filter (AMNLM) was first evaluated using a computer phantom and a physical phantom to simulate realistic but challenging situations where small lesions are located in homogeneous regions, which can be detected on PET but not on CT. The proposed method was further assessed with a clinical study of a patient with lung lesions. The performance of the proposed method was compared to Gaussian, edge-preserving bilateral and NLM filters, as well as median nonlocal means (MNLM) filtering without an anatomical prior. The proposed AMNLM method yielded improved lesion contrast and SNR compared with other methods even with imperfect anatomical knowledge, such as missing lesion boundaries and mismatched organ boundaries.
Fully automated scoring of chest radiographs in cystic fibrosis

We present a prototype of a fully automated scoring system for chest radiographs (CXRs) in cystic fibrosis. The system was used to analyze real, clinical CXR data, to estimate the Shwachman-Kulczycki score for the image. Images were resampled and normalized to a standard size and intensity level, then segmented with a patch-based nearest-neighbor mapping algorithm. Texture features were calculated regionally and globally, using Tamura features, local binary patterns (LBP), gray-level co-occurrence matrix and Gabor filtering. Feature selection was guided by current understanding of the disease process, in particular the reorganization and thickening of airways. Combinations of these features were used as inputs for support vector machine (SVM) learning to classify each CXR, and evaluated using two-fold cross-validation for agreement with clinician scoring. The final computed score for each image was compared with the score assigned by a physician. Using this prototype system, we analyzed 139 CXRs from an Australian pediatric cystic fibrosis registry, for which texture directionality showed greatest discriminating power. Computed scores agreed with clinician scores in 75% of cases, and up to 90% of cases in discriminating severe disease from mild disease, similar to the level of human interobserver agreement for this dataset.
Multiscale deformable registration using edge preserving scale space for adaptive radiation therapy

Registration of planning images with daily images is an important component for adaptive radiation therapy (ART). In this paper, a multiscale deformable registration framework is proposed by combining edge preserving scale space with the free form deformation (FFD) for registration of planning computed tomography (CT) images with daily cone beam CT (CBCT) images. The edge preserving scale space which is able to select edges and contours of an image according to their geometric size is derived from the total variation model with the L1 norm (TV-L1). At each scale, the selected edges and contours are sufficiently strong to drive the deformation using the FFD grid, then the deformation fields are gained by a coarse to fine manner. Furthermore, for automated registration we design an optimal estimation of the TV-L1 parameter by minimizing the defined offset. The experiments on CT and CBCT images show accuracy and robustness when compared to traditional methods.
In-Shoe Plantar Pressure Measurement and Analysis System Based on Fabric Pressure Sensing Array

Spatial and temporal plantar pressure distributions are important and useful measures in footwear evaluation, athletic training, clinical gait analysis, and pathology foot diagnosis. However, present plantar pressure measurement and analysis systems are more or less uncomfortable to wear and expensive. This paper presents an in-shoe plantar pressure measurement and analysis system based on a textile fabric sensor array, which is soft, light, and has a high-pressure sensitivity and a long service life. The sensors are connected with a soft polymeric board through conductive yarns and integrated into an insole. A stable data acquisition system interfaces with the insole, wirelessly transmits the acquired data to remote receiver through Bluetooth path. Three configuration modes are incorporated to gain connection with desktop, laptop, or smart phone, which can be configured to comfortably work in research laboratories, clinics, sport ground, and other outdoor environments. A real-time display and analysis software is presented to calculate parameters such as mean pressure, peak pressure, center of pressure (COP), and shift speed of COP. Experimental results show that this system has stable performance in both static and dynamic measurements.
Space Vector Modulation for High-Power Three-Level NPC Rectifiers Without Even Order Harmonics

Space vector modulation (SVM) is a preferred PWM scheme for multilevel converters mainly due to its flexibility, easy digital implementation and good harmonic profile. However, the conventional SVM scheme normally produces even-order voltage/current harmonics at the rectifier inputs. The even-order harmonic currents are strictly regulated by the harmonic guidelines such as the IEEE Standards 519-1992 in North America. In this paper, the mechanism of even-order harmonic generation is analyzed, and a new SVM scheme that has the ability to eliminate these harmonics is proposed for three-level neutral point clamped (NPC) rectifiers. The harmonic profile of both schemes is investigated by computer simulation. The effectiveness of the proposed scheme is experimentally verified
Use of anatomical priors in the segmentation of PET lung tumor images

Advances in dual-modality imaging that combine anatomical and functional images have considerably improved tumor staging and treatment planning. PET/CT can, for example, detect tumor invasion into adjacent tissues, as well as provide precise localization of lesions, even when no morphological changes are identified by CT. In lung cancer staging and therapy planning, determination of a tumor's size, its invasion into adjacent structures, mediastinal node status, and the detection of distant metastases are of great importance. In this study, we investigated the use of anatomical priors in the segmentation of tumors in simulated functional PET images of the lungs. The anatomical information was used as priors to extract the lung structures from the co-aligned PET data. The performance of a conventional iterative pixel-classification algorithm of fuzzy c-means (FCM) cluster analysis for segmenting the PET data with and without the use of the priors was quantitatively evaluated. A Monte Carlo simulation of PET with anatomical priors derived from the Zubal whole-body phantom was used in the evaluation. We demonstrate that the use of the anatomical priors to restrict the PET data to regions of interest consisting only of lung structures is able to improve the accuracy and reliability of the cluster analysis segmentation of lung tumors in PET images.
Exploration of Virtual and Augmented Reality for Visual Analytics and 3D Volume Rendering of Functional Magnetic Resonance Imaging (fMRI) Data

Statistical analysis of functional magnetic resonance imaging (fMRI), such as independent components analysis, is providing new scientific and clinical insights into the data with capabilities such as characterising traits of schizophrenia. However, with existing approaches to fMRI analysis, there are a number of challenges that prevent it from being fully utilised, including understanding exactly what a 'significant activity' pattern is, which structures are consistent and different between individuals and across the population, and how to deal with imaging artifacts such as noise. Interactive visual analytics has been presented as a step towards solving these challenges by presenting the data to users in a way that illuminates meaning. This includes using circular layouts that represent network connectivity and volume renderings with 'in situ' network diagrams. These visualisations currently rely on traditional 2D 'flat' displays with mouse-and-keyboard input. Due to the constrained screen space and an implied concept of depth, they are limited in presenting a meaningful, uncluttered abstraction of the data without compromising on preserving anatomic context. In this paper, we present our ongoing research on fMRI visualisation and discuss the potential for virtual reality (VR) and augmented reality (AR), coupled with gesture-based inputs to create an immersive environment for visualising fMRI data. We suggest that VR/AR can potentially overcome the identified challenges by allowing for a reduction in visual clutter and by allowing users to navigate the data abstractions in a 'natural' way that lets them keep their focus on the visualisations. We present exploratory research we have performed in creating immersive VR environments for fMRI data.
An Adaptive Recognition Model for Image Annotation

In this paper, an adaptive recognition model (ARM) is proposed for image annotation. The ARM consists of an adaptive classification network (CFN) and a nonlinear correlation network (CLN). The adaptive CFN aims to annotate an image with keywords, and the CLN is used to unveil the correlative information of keywords for annotation refinement. Image annotation is carried out by an ARM in two stages. In the first stage, the features extracted from regions of the input image are fed to a CFN to produce classification labels. In the second stage, the CLN uses keyword correlations learned from the training images to refine the classification result. The ARM works in a forward-propagating manner, resulting in high efficiency in image annotation. Furthermore, the computational time of an ARM is insensitive to the number of regions of the input image and the vocabulary size. In this paper, the effect of keyword correlation in image annotation is, comprehensively, investigated on a real image dataset and a synthetic image dataset. The exploitation of a controllable synthetic dataset helps to systematically study the function of keyword correlation and effectively analyze the performance of the ARM. Experimental results demonstrate the efficiency and effectiveness of the ARM.
Extracting occlusion from images based on texture-synthesis methods

This paper proposes a method of region segmentation by which occlusions are extracted from a non-repeating, non-parametric texture. Based on a method of texture synthesis proposed by Efros and Leung (1999), this method of segmentation assumes the texture can be described by a Markov random field with probabilities conditioned on neighboring pixels. Instead of generating a new texture, the Markov model is used to determine the acceptance of pixels as part of the texture. This method has the advantage of not requiring any special filter preprocessing or texton processing.
Efficient blind blur identification using discrete periodic Radon transform

The problem of restoring an image from its convolution with an unknown blur function is a well-known problem in image processing. Many approaches have been proposed to solve the problem and they have shown to have good performance in identifying the blur function or retrieving the original image. However, in actual implementation, various problems incurred due to the large data size and long computational time of these approaches are undesirable, even with the current computing machines. An efficient algorithm is proposed for multichannel blind blur identification based on the discrete periodic Radon transform (DPRT). With the DPRT, the original 2-dimensional multichannel blind blur identification problem can be converted into 1-dimensional probelms, which greatly reduces the memory size and computational time required. Experimental results show that a 44% reduction in the number of operations can be achieved as compared to the traditional approach.
Nonlinear curvelet diffusion for noisy image enhancement

Digital image degradation normally arises during image acquisition and processing, which has a direct influence on the visual quality of the image. This paper proposes a combined method for enhancement of noisy image by using the mirror-extended curvelet transform and nonlinear anisotropic diffusion. First, an improved enhancement function is proposed to nonlinearly shrink and stretch the curvelet coefficients. Then, the enhanced results are further processed by the nonlinear diffusion where only the nonsignificant, i.e., nonthresholded, curvelet coefficients are changed by means of a diffusion process in order to reduce the pseudo-Gibbs artifacts. Experimental results indicate the proposed method has better performances to enhance the shape of edges and important detailed features as well as suppress noise, in comparison to the curvelet-based enhancement method without diffusion and the wavelet-based enhancement methods with/without diffusion.
Message from ICME 2012 General Chairs

ICME 2012 is the thirteen in the series of ICME conferences that has been held annually since 2000, in various cities throughout the world. The success of this conference would not have been possible without the generous help of sponsors. Paper prizes and Student Travel Grants are sponsored by the National Information and Communications Technology Australia (NICTA), Microsoft Research, IBM Research, Canon Information Systems Research Australia (CiSRA), and Advanced Analytics Institute (AAI) at the University of Technology, Sydney (UTS). ICME 2012 features a new plenary session ? Time Machine! The session consists of a series of expert presentations that re-introduce ideas published "before their time" and, as a result, their impact has not yet been fully realized. ICME 2012 also has outstanding lectures including keynote lectures and research overviews. ICME 2012 will offer several paper prizes, including Best Paper Award, Best Student Paper Award, and Best Demo Award.
End-User Development for Interactive Data Analytics: Uncertainty, Correlation and User Confidence

This paper investigates End-User Development (EUD) for interactive data-analytic interfaces—building upon the ideas of making machine learning transparent. The research is carried out in a business operation environment (water pipe failure prediction in our case) motivated to integrate advanced analytics into decision-making processes of an urban Internet of Things (IoT) concept. We explore effects of revealing uncertainty and correlation on user confidence in a data-driven decision making scenario. It was found that user confidence varied significantly amongst various user groups when different machine learning models were displayed with/without supplementary information. Galvanic Skin Response (GSR) signals were analyzed and shown as reasonable indices for predicting user confidence levels. Supplementary data visualizations (of inherent uncertainty and correlation in data) contributed to explicability principles while GSR indexing added towards correctibility principles. We recommend transparent machine learning as the key to effective EUD for interactive data analytics.
Hierarchical and binary spatial descriptors for lung nodule image retrieval

With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance of the hierarchical spatial descriptor, as well as presents a new binary descriptor for the retrieval of lung nodule images. The descriptors were evaluated on the ELCAP public access database, exhibiting good performance overall.