nils-herrmann/clean_scirepeval huggingface dataset:
Robust saliency detection via regularized random walks ranking

In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image. In this paper, we propose a novel bottom-up saliency detection approach that takes advantage of both region-based features and image details. To provide more accurate saliency estimations, we first optimize the image boundary selection by the proposed erroneous boundary removal. By taking the image details and region-based estimations into account, we then propose the regularized random walks ranking to formulate pixel-wised saliency maps from the superpixel-based background and foreground saliency estimations. Experiment results on two public datasets indicate the significantly improved accuracy and robustness of the proposed algorithm in comparison with 12 state-of-the-art saliency detection approaches.
An automated elastic image registration method for CT and MR brain images

In this paper, an automated approach to register CT and MR brain images based on mutual information and landmark features is described. Images are first rigidly registered by maximization of mutual information. A geometrical feature is then exploited by a differential geometrical operator. Landmarks are searched in the geometrical feature and registered elastically by thin-plate splines. Experimental results of 2D MR-TI to CT and MR-T2 to CT registration are presented.
Robust Model for Segmenting Images With/Without Intensity Inhomogeneities

Intensity inhomogeneities and different types/levels of image noise are the two major obstacles to accurate image segmentation by region-based level set models. To provide a more general solution to these challenges, we propose a novel segmentation model that considers global and local image statistics to eliminate the influence of image noise and to compensate for intensity inhomogeneities. In our model, the global energy derived from a Gaussian model estimates the intensity distribution of the target object and background; the local energy derived from the mutual influences of neighboring pixels can eliminate the impact of image noise and intensity inhomogeneities. The robustness of our method is validated on segmenting synthetic images with/without intensity inhomogeneities, and with different types/levels of noise, including Gaussian noise, speckle noise, and salt and pepper noise, as well as images from different medical imaging modalities. Quantitative experimental comparisons demonstrate that our method is more robust and more accurate in segmenting the images with intensity inhomogeneities than the local binary fitting technique and its more recent systematic model. Our technique also outperformed the region-based Chan-Vese model when dealing with images without intensity inhomogeneities and produce better segmentation results than the graph-based algorithms including graph-cuts and random walker when segmenting noisy images.
An Ontology for the Integration of Multiple Genetic Disorder Data Sources

As a huge amount of gene disorder information is available on the Internet, there is an increasing requirement to integrate these data sources. The integration of gene disorder data sources provides an important tool in the research of life science, therapeutics, and genetic disease prevention and inhibition. The key challenge of such integration is how to deal with semantic heterogeneity of multiple information resources. The paper proposes an ontology-based approach to describe and extract the semantics of genetic disorder terminologies and provides a mechanism for sharing and reusing genetic disorder knowledge. According to this unified meta model, heterogeneous gene disorder data sources can be integrated, and a semantic middleware has the ability to do reasoning on the knowledge base of gene disorder for users and applications' various queries
Content access and distribution of multimedia medical data in E-health

E-health is greatly impacting on information distribution and availability within the health services, hospitals and to the public. Previous research has addressed the development of system architectures with the aim of integrating the distributed and heterogeneous medical information systems. Easing the difficulties in the sharing and management of multimedia medical data and the timely accessibility to these data are critical needs for health care providers. We have proposed a client-server agent that integrates and allows a portal to every permitted information system of the hospital that consists of picture archiving and communication systems (PACS), radiology information system (RIS) and hospital information system (HIS) via the intranet and the Internet. Our proposed agent enables remote access into the usually closed information system of the hospital and a server that manages all the multimedia medical data and allows for in-depth and complex search queries for content access and automatic creation of patient reports for distribution.
Volume of interest (VOI) feature representation and retrieval of multi-dimensional dynamic positron emission tomography images

Medical images often require domain knowledge and diagnostic features in order to measure the similarity and relevance. It is unlikely that conventional image feature representation can adequately represent the volumetric and diagnostic properties of the medical images for content-based retrieval. In this study, we present a volume of interest (VOI) feature representation and retrieval of multi-dimensional dynamic positron emission tomography (PET) images consisting of 3D spatial and 1D temporal domains. The VOIs are represented by location based on the 3D standard atlas, physiological temporal kinetics and parametric estimate, volume, and surface distribution by utilizing both the spatial and temporal domain, in addition to the textual information. The proposed representation of the features can allow for effective retrieval by domain specific image features and are applicable for content-based image retrieval (CBIR).
ADM: a dynamic model for general multimedia storage and content-based retrieval

Content-based retrieval (CBR) has been considered effective for searching complex multimedia data from the Internet. Since multimedia has plentiful connotations, the user-dependent content definitions always are arbitrary but insufficient for general applications. In this paper, a new attribute-dispersed model (ADM) is proposed for general multimedia storage and content-based retrieval. According to the proposed model, different attributes of the multimedia object are firstly classified as independent ones and dependent ones, the later will be stored and managed separately, and new attributes or concepts can be easily expanded dynamically in a self-description and reference list (SDRL) scheme. It is expected that the proposed model can be a rational and practical solution for the efficient and effective storage and retrieval of general multimedia information.
Combined constraint for single image dehazing

Existing single image dehazing techniques have limitations on estimating accurate transmission maps for pixels which do not meet the assumption of the dark channel prior. A combined constraint is presented for single image dehazing by taking both geometric and natural characteristics of outdoor images into consideration. The combined constraint can be applied to various regularisation-based optimisation frameworks. Both qualitative and quantitative comparisons with the state-of-the-art dehazing approaches indicate the superiority of the proposed constraint.
Photovoltaic response for high density InGaAs coupled quantum dots

In order to enhance absorption at infrared range for GaAs based solar cell, we insert the nine-layer of vertically coupled quantum dots (VCQDs) into the active layer. We fine modulated the GaAs spacer thickness of coupled In<sub>0.75</sub>Ga<sub>0.25</sub>As QDs, and investigated the effects on photovoltaic response. For the open-circuit voltage (V<sub>oc</sub>), the values decreases from 0.61 V to 0.55 V as the spacer thickness (d) decreases from d= 15 nm to 5 nm for the nine-layer VCQDs solar cells. The reduction of V<sub>oc</sub> for the VCQDs solar cells is attributed to the accumulation of compressive strain energy in the active QD region. For the sample of d=10 nm shows the best performance of current density (J<sub>sc</sub>~24 mA/cm<sup>2</sup>) and efficiency (η~10.6). The J<sub>sc</sub> and η are increases by 55% and 112% more than the device without QDs, respectively.
Poster: Shake menus: Towards activation and placement techniques for prop-based 3D graphical menus

Shake menus are a novel method for activating, displaying, and selecting options presented relative to a tangible object or manipulator in a 3D user interface. They provide ready-to-hand interaction, including facile selection and placement of objects. We present the technique, several alternative methods for presenting shake menus (world-referenced, display-referenced, and object-referenced), and an evaluation of menu placement.
Classification of thresholded regions based on selective use of PET, CT and PET-CT image features

Fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is the preferred image modality for lymphoma diagnosis. Sites of disease generally appear as foci of increased FDG uptake. Thresholding methods are often applied to robustly separate these regions. However, its main limitation is that it also includes sites of FDG excretion and physiological FDG uptake regions, which we define as FEPU - sites of FEPU include the bladder, renal, papillae, ureters, brain, heart and brown fat. FEPU can make image interpretation problematic. The ability to identify and label FEPU sites and separate them from abnormal regions is an important process that could improve image interpretation. We propose a new method to automatically separate and label FEPU sites from the thresholded PET images. Our method is based on the selective use of features extracted from data types comprising of PET, CT and PET-CT. Our FEPU classification of 43 clinical lymphoma patient studies revealed higher accuracy when compared to non-selective image features.
Texture analysis and retrieval using fractal signature and B-spline wavelet transform with second order derivative

In the paper, we proposed a novel over-complete B-spline wavelet transform and fractal signature for texture image analysis and retrieval. Traditionally, discrete wavelet frame took the first order derivative of smoothing function into account, which is equivalent to Canny edge detection. The second order derivative spline wavelet has the ability to detect the variation of the edge width, and the finite impulse response is conducted in the paper. Additionally, statistical features in wavelet domain and fractal signature are utilized in the retrieval. Experimental results have shown that the proposed method is reasonable to describe the essences of the textures and can reach the highest retrieval rate (76.54%) comparing with Gabor Filter and first order derivative over-complete wavelet transform.
A Scalable Approach for Content-Based Image Retrieval in Peer-to-Peer Networks

Peer-to-peer networking offers a scalable solution for sharing multimedia data across the network. With a large amount of visual data distributed among different nodes, it is an important but challenging issue to perform content-based retrieval in peer-to-peer networks. While most of the existing methods focus on indexing high dimensional visual features and have limitations of scalability, in this paper we propose a scalable approach for content-based image retrieval in peer-to-peer networks by employing the bag-of-visual-words model. Compared with centralized environments, the key challenge is to efficiently obtain a global codebook, as images are distributed across the whole peer-to-peer network. In addition, a peer-to-peer network often evolves dynamically, which makes a static codebook less effective for retrieval tasks. Therefore, we propose a dynamic codebook updating method by optimizing the mutual information between the resultant codebook and relevance information, and the workload balance among nodes that manage different codewords. In order to further improve retrieval performance and reduce network cost, indexing pruning techniques are developed. Our comprehensive experimental results indicate that the proposed approach is scalable in evolving and distributed peer-to-peer networks, while achieving improved retrieval accuracy.
Fenchel Duality Based Dictionary Learning for Restoration of Noisy Images

Dictionary learning based sparse modeling has been increasingly recognized as providing high performance in the restoration of noisy images. Although a number of dictionary learning algorithms have been developed, most of them attack this learning problem in its primal form, with little effort being devoted to exploring the advantage of solving this problem in a dual space. In this paper, a novel Fenchel duality based dictionary learning (FD-DL) algorithm has been proposed for the restoration of noise-corrupted images. With the restricted attention to the additive white Gaussian noise, the sparse image representation is formulated as an ℓ2-ℓ1 minimization problem, whose dual formulation is constructed using a generalization of Fenchel's duality theorem and solved under the augmented Lagrangian framework. The proposed algorithm has been compared with four state-of-the-art algorithms, including the local pixel grouping-principal component analysis, method of optimal directions, K-singular value decomposition, and beta process factor analysis, on grayscale natural images. Our results demonstrate that the FD-DL algorithm can effectively improve the image quality and its noisy image restoration ability is comparable or even superior to the abilities of the other four widely-used algorithms.
Graph cuts based relevance feedback in image retrieval

Relevance feedback (RF) allows users to be actively involved in the information retrieval process and has been widely used in various information retrieval tasks. While most existing RF methods in content-based image retrieval (CBIR) focus on visual features of individual images only, in this paper we formulate the relevance feedback process as an energy minimization problem. The energy function takes into account both the feature aspect of each image and the manifold structure among individual images. The solution of labelling images as relevant or irrelevant is obtained with the graph cuts method. As a result, our method enables flexibly partitioning the feature space and labelling of images and is capable of handling challenging scenarios (or queries). Experimental results demonstrate that our proposed method outperforms the popular RF methods.
Face recognition method using mutual information and hybrid feature

A two-stage face recognition algorithm is proposed. In the first stage, the mutual information match is applied to reduce the candidate pattern amount in the database. In the second stage, the principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the probe image to extract the corresponding features, which will be used in classification. The approach is tested on ORL database, Shimon database and Harvard database. The experimental results demonstrate that the performance of this system is superior to other algorithms on the same database.
Automatic Descending Aorta Segmentation in Whole-Body PET-CT Studies for PERCIST-Based Thresholding

Medical imaging is a fundamental component of modern healthcare where majority of medical conditions can benefit from some kinds of imaging. A dual-modal positron emission tomography - computed tomography (PET-CT) has increasingly become the preferred imaging method to stage the common cancers and to assess treatment response. For example, PET image can quantitatively assess the treatment before morphological changes that can be detected on anatomical CT image. PET response criterion (PERCIST) is a widely recognised thresholding method for detecting malignant lesions (high metabolic value) in treatment response. It is based on the calculation of standardised uptake value with lean body mass (SUVLBM), together with a volume of interest (VOI) reference placed on the right lobe of the liver or the descending aorta (when the liver is abnormal e.g., liver cancer). These two structures are considered to have stable metabolism among the PET patient population and therefore can be used to normalise the SUV. The current practice of PERCIST thresholding depends on the manual delineation of the VOI reference which is a time consuming and operator-dependent process. Furthermore, such VOI selection is more difficult on the smaller descending aorta structure when compared to the liver. In this study, we propose a fully automatic approach to the segmentation of the descending aorta for use in the calculation of the PERCIST thresholding. A multi-atlas registration coupled with weighted decision function was used in the whole-body CT for the segmentation of the descending aorta, with the resulting VOI reference mapped to the co-aligned PET counterpart. We evaluated our method with 30 clinical PET-CT studies with preliminary results demonstrating reliability and robustness.
Broadband quantum dots-in-a-well solar cells

The conversion efficiency of broadband InGaAs quantum dots-in-a-well (DWell) solar cells has been improved by optimizing the p-type doping and growth temperature for the DWell. The solar efficiency (η) is 130% times of the counterpart of GaAs baseline cell. Moreover we obtain the best photovoltaic result by inserting matrix pattern instead grid pattern and using wet etching to enhance the conversion efficiency. The photovoltaic characteristics shows an efficiency of 10.86%.
Improved non-invasive quantification of physiological processes with dynamic PET using blind system identification

The traditional quantitative study using tracer kinetic modeling requires repeated blood sampling to measure the tracer concentration in plasma. It is invasive, time-consuming and costly. We propose a new approach to estimate physiological parameters for dynamic Positron Emission Tomography (PET) without taking blood samples. With the new approach, the quantification problem is first converted to a discrete blind system identification problem. A multi-channel blind identification technique is applied to estimate the required system parameters. A Monte Carlo simulation is carried out for the proposed approach on estimating the regional cerebral metabolic rate of glucose (rCMRGlc) with the fluoro-deoxy-2-glucose (FDG) model. The results show that the proposed approach can estimate the required physiological parameters in a comparable manner to that of traditional invasive approaches.
Leaf Vein Extraction Using Independent Component Analysis

The purpose of this work is to develop an interactive tool which helps botanists to extract the vein system with its hierarchical properties with as little user interaction as possible. In this paper, we present a new venation extraction method using independent component analysis (ICA). The popular and efficient FastICA algorithm is applied to patches of leaf images to learn a set of linear basis functions or features for the images and then the basis functions are used as the pattern map for vein extraction. In our experiments, the training sets are randomly generated from different leaf images. Experimental results demonstrate that ICA is a promising technique for extracting leaf veins and edges of objects. ICA, therefore, can play an important role in automatically identifying living plants.
Microscopic Image Segmentation with Two-Level Enhancement of Feature Discriminability

Microscopic cellular image segmentation has become one of the most important routine procedures in modern biological applications. The segmentation task is non-trivial, however, mainly due to imaging artifacts causing highly inhomogeneous appearances of cell nuclei and background with large intensity variations within and across images. Such inconsistent appearance profiles would cause feature overlapping between cell nuclei and background pixels and hence lead to misclassifiation. In this paper, we present a novel method for automatic cell nucleus segmentation, focusing on tackling the intensity inhomogeneity issue. A two-level approach is designed to enhance the discriminative power of intensity features, by first a reference-based intensity normalization for reducing the inter-image variations, and then a further localized object discrimination for overcoming the intra-image variations. The proposed method is evaluated on three different sets of 2D fluorescence microscopy images, and encouraging performance improvements over the state-of-the-art results are obtained.
Boosted multifold sparse representation with application to ILD classification

Classification performance with sparse representation is largely affected by the low discriminative power of image features. In this study, we propose a new sparse representation model, namely the Boosted Multifold Sparse Representation (BMSR), to improve the classification performance. By dividing the training set into multiple subsets, sparse representation using one subset is used as a weak classifier. A threefold boosting approach is then designed to combine the multiple weak classifiers to create the final class label. We applied the BMSR method to classify image patches of different interstitial lung disease (ILD) patterns using a publicly available dataset. Promising performance improvement over non-boosted sparse representation is shown.
Matching Visual Saliency to Confidence in Plots of Uncertain Data

Conveying data uncertainty in visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. This paper proposes a methodology for efficiently generating density plots of uncertain multivariate data sets that draws viewers to preattentively identify values of high certainty while not calling attention to uncertain values. We demonstrate how to augment scatter plots and parallel coordinates plots to incorporate statistically modeled uncertainty and show how to integrate them with existing multivariate analysis techniques, including outlier detection and interactive brushing. Computing high quality density plots can be expensive for large data sets, so we also describe a probabilistic plotting technique that summarizes the data without requiring explicit density plot computation. These techniques have been useful for identifying brain tumors in multivariate magnetic resonance spectroscopy data and we describe how to extend them to visualize ensemble data sets.
Generalized regional disorder-sensitive-weighting scheme for 3D neuroimaging retrieval

3D functional neuroimaging is used in the diagnosis and management of neurological disorders. The efficient management and analysis of these large imaging datasets has prompted research in the field of content-based image retrieval. In this context, our generalized regional disorder-sensitive-weighting (DSW) scheme gives greater weight to brain regions affected by the diseases than regions that are relatively spared. We used two DSW matrices; one matrix is based on the occurrence maps that highlight abnormal functional regions; the other is based on the regional Fisher discriminant ratio. Our results suggest that our DSW matrices enhance neuroimaging data retrieval and provide a flexible weighting solution for the clinical analysis of different types of neurological disorders.