nils-herrmann/clean_scirepeval huggingface dataset:
Rough Sets and Near Sets in Medical Imaging: A Review

This paper presents a review of the current literature on rough-set- and near-set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction, and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines, and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a promising approach to solving the medical image correspondence problem as well as an approach to classifying perceptual objects by means of features in solving medical imaging problems. Other generalizations of rough sets such as neighborhood systems, shadowed sets, and tolerance spaces are also briefly considered in solving a variety of medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.
HEp-2 Cell Classification Using Multi-dimensional Local Binary Patterns and Ensemble Classification

Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. This is particularly useful for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications on different autoimmune diseases. This categorisation is typically performed by manual evaluation which is time consuming and subjective. In this paper, we present a method for automatic classification of HEp-2 cells using local binary pattern (LBP) based texture descriptors and ensemble classification. In our approach, we utilise multi-dimensional LBP (MD-LBP) histograms, which record multi-scale texture information while maintaining the relationships between the scales. Our dedicated ensemble classification approach is based on a set of heterogeneous base classifiers obtained through application of different feature selection algorithms, a diversity based pruning stage and a neural network classifier fuser. We test our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to outperform all algorithms that were entered in the competition as well as to exceed the performance of a human expert.
Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images

Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions.
Robust codebook-based video background subtraction

Dynamic backgrounds and sudden illumination changes are two of the major problems associated with background subtraction techniques. In this paper, we present a novel approach to background subtraction that addresses both of these challenges. In particular, we present an improved codebook background modelling and subtraction technique. We utilise image segmentation on the background image and model the background with a codebook for each pixel along with a pseudo background layer. We perceive background motion as an occlusion of one background layer by a nearby background layer. In other words, sliding of one background layer over a neighbouring layer causes background motion and will hence result in false segmentation. We present our approach of codeword spreading across layer boundaries to handle background motion. Furthermore, we present a two-step update of the background codebook to handle both sudden and gradual illumination changes.
An effective algorithm for multiagent dispatching of resources in heterogeneous cloud environments

In this paper, we deal with the problem of adaptive dispatching (distribution) of resources in a cloud computer environment (CCE). In particular, the CCE comprises heterogeneous computational resources and we consider the implementation of a flow of large-scale scientific tasks consisting of a set of information-dependent subtasks that enter the CCE at random times. We present an effective method for multiagent dispatching of CCE resources that can be employed when CCE resources have different performances for different tasks and different channel bandwidths for connecting with cloud infrastructure. We give a formal definition of the problem of CCE dispatching for these conditions and suggest principles of its solution using a set of program agents that are physically implemented on separate CCE resources and that represent their “interests” during dispatch. We introduce the developed algorithm of a program agent as a multiagent dispatcher providing adaptive distribution of all currently free CCE computational resources among the tasks of the flow, taking into consideration their real performances and channel capacities. We further present experimental results obtained using a program model of a heterogeneous CCE.
Image browsers — Effective and efficient tools for managing large image collections

Visual information is becoming increasingly important and tools to manage these vast repositories of media collections are highly sought after. In this paper, we focus on images and image databases and how to effectively and efficiently access these. In particular, we present the idea of image browsers which provide an intuitive and interactive means of handling image collections. Not only do image browsers pose a useful alternative to retrieval-based systems, they also provide a visualisation of the whole image collection and let users explore particular parts of the collection. Apart from introducing the basic concepts and approaches of different image browsing approaches, we present two particular systems, the Hue Sphere Image Browser and the Honeycomb Image Browser, which were developed in our lab, as well as their recent ports to a large multi-touch environment for truly interactive image database navigation.
Convex and non-convex illuminant constraints for dichromatic colour constancy

The dichromatic reflectance model introduced by S. Shafer (1985) predicts that the colour signals of most materials fall on a plane spanned by a vector due to the material and a vector that represents the scene illuminant. Since the illuminant is in the span of all dichromatic planes, colour constancy can be achieved by finding the intersection of two or more planes. Unfortunately, this approach has proven to be hard to get to work in practice. First, segmentation needs to be carried out and second, the actual intersection computation is quite unstable: small changes in the orientation of a dichromatic plane can significantly alter the location of the intersection point. We propose to ameliorate the instability problem by regularising the intersection. Specifically, we introduce a constraint on the colour of the illuminant. We show how the intersection problem in the context of convex and non-convex illuminant constraints, based on the distribution of common light sources, can be solved. This algorithm coupled with the simplest of segmentations results in good estimation results for a large set of real images. Estimation performance is significantly better than for the unconstrained algorithm.
Image retrieval on the Honeycomb Image Browser

Efficient and effective approaches of dealing with the vast amount of visual information available nowadays are highly sought after. This is particularly the case for image collections, both personal and commercial. Due to the magnitude of these ever expanding image repositories, annotation of all images images is infeasible, and search in such an image collection therefore becomes inherently difficult. Although content-based image retrieval techniques have shown much potential, such approaches also suffer from various problems making it difficult to adopt them in practice. In this paper, we follow a different approach, namely that of browsing image databases for image retrieval. In our Honeycomb Image Browser, large image databases are visualised on a hexagonal lattice with image thumbnails occupying hexagons. Arranged in a space filling manner, visually similar images are located close together enabling large image datasets to be navigated in a hierarchical manner. Various browsing tools are incorporated to allow for interactive exploration of the database. Experimental results confirm that our approach affords efficient image retrieval.
An on-line tool for browsing large image repositories

Image collections are growing rapidly, and effective tools to manage and interact with these repositories are highly sought after. In this paper we present an on-line tool, implemented in Adobe Flash®, for exploring large image databases in an intuitive and efficient manner. In contrast to other approaches, which typically present images in a linear fashion, we employ a spherical visualisation space onto which we project image thumbnails (either on the inside or the outside of the sphere). Importantly, images are arranged so that visually similar images are located close to each other on the sphere. Organisation on a grid layout prevents image overlap and hence contributes to a clear presentation. Large image databases are handled through a hierarchical storage structure where each image in the visualisation can correspond to a cluster of images that can be expanded by the user.
Robust texture retrieval of compressed images

Almost all images are stored in compressed form, most commonly in (lossy) JPEG format. In this paper, we show that compression leads to a drop in performance of texture retrieval algorithms, and propose a method that reverses this performance drop. We achieve this by what might at first glance seem counter-intuitive, namely by compressing the images even more. In particular, we recompress images (or rather re-quantising their DCT coefficients) to their lowest common image quality setting. We demonstrate, on a large benchmark texture retrieval database and using standard texture algorithms, that this results in improved image retrieval performance close to that obtained on uncompressed images.
Application of hybrid data mining methods to increase profitability of heavy oil production

In this paper, we consider the problem of increasing the efficiency of heavy oil production by a qualitative enhanced oil recovery (EOR) application. A novel approach, based on the analysis of data related to successful events, is proposed. In particular, a synthesised model, targeted to automated search of wells for EOR application, is developed. For data processing, we present a novel approach based on a hybrid implementation of neural network analysis techniques and evolutionary algorithms. The presented approach enables the selection of EOR in fuzzy, difficult to formalise, oilfield conditions and consequently reduces the dependency on human factors.
Fusion of multi-spectral and panchromatic satellite images using principal component analysis and fuzzy logic

In this paper, we propose a fuzzy-based multi-spectral (MS) and panchromatic (PAN) image fusion approach which provides a tradeoff solution between spectral and spatial fidelity and is able to preserve more detail in terms of spectral and spatial information. First, we perform principal component analysis on the multi-spectral images and utilise the first principal component to extract matched low and high frequency coefficients. We then apply fuzzy-based image fusion rules to fuse the first principal component with the PAN image, followed by fusing the approximation coefficients. The proposed approach is tested on several satellite images and shown to provide a feasible and effective approach.
Combining one-class classifiers for imbalanced classification of breast thermogram features

Thermography provides an interesting modality for diagnosing breast cancer as it is a non-contact, non-invasive and passive technique that is able to detect small tumors, which in turn can lead to earlier diagnosis. We perform computer-aided diagnosis of breast thermograms based on image features describing bilateral differences in regions of interest and a pattern classification approach that learns from previous examples. As is often the case in medical diagnosis, such training sets are imbalanced as typically (many) more benign cases get recorded compared to malignant cases. In this paper, we address this problem and perform classification using an ensemble of one-class classifiers. One-class classification uses samples from a single distribution to derive a decision boundary, and employing this method on the minority class can significantly boost its recognition rate and hence the sensitivity of our approach. We combine several one-class classifiers using a random subspace approach and a diversity measure to select members of the committee. We show that our proposed technique works well and leads to significantly improved performance compared to a single one-class predictor as well as compared to state-of-the-art classifier ensembles for imbalanced data.
A pruned ensemble classifier for effective breast thermogram analysis

Thermal infrared imaging has been shown to be useful for diagnosing breast cancer, since it is able to detect small tumors and hence can lead to earlier diagnosis. In this paper, we present a computer-aided diagnosis approach for analysing breast thermograms. We extract image features that describe bilateral differences of the breast regions in the thermogram, and then feed these features to an ensemble classifier. For the classification, we present an extension to the Under-Sampling Balanced Ensemble (USBE) algorithm. USBE addresses the problem of imbalanced class distribution that is common in medical decision making by training different classifiers on different subspaces, where each subspace is created so as to resemble a balanced classification problem. To combine the individual classifiers, we use a neural fuser based on discriminants and apply a classifier selection procedure based on a pairwise double-fault diversity measure to discard irrelevant and similar classifiers. We demonstrate that our approach works well, and that it statistically outperforms various other ensemble approaches including the original USBE algorithm.
Compactifying multi-dimensional LBP variance texture descriptors based on DCT and feature reduction

Texture analysis and classification have received significant research interest and have been shown to be essential in many computer vision systems and applications. Local binary patterns (LBP) are powerful yet simple texture descriptors which describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture description. Furthermore, local contrast information can be integrated into LBP leading to LBP variance (LBPV) features. In conventional LBP methods, the histograms corresponding to different radii are simply concatenated resulting in a loss of information between different resolutions and added ambiguity. Multi-dimensional LBPV (MD-LBPV) preserves the relationships between the scales by building a multi-dimensional histogram of LBPV patterns and can lead to improved texture classification. In this paper, we address the relatively large feature length of MD-LBPV descriptors, and show that feature reduction based on discrete cosine transform (DCT) combined with principal component analysis (PCA) can yield effective and compact texture descriptors with high classification accuracy.
Investigating the influence of thunderstorms on atmospheric electric field potential gradient variations

In this paper, we simultaneously investigate daily variations of the atmospheric electric field during thunderstorms and lightning parameters within the zone of representative operation of electric field mills, to allow for detailed spatial-temporal analysis of electric field variations. For this purpose, we develop a firmware complex for processing of experimental data of the lightning sensor and electric field mill networks, and software implementations for data mining, transferring and processing of the relevant data. Experimental results of atmospheric electric fields during lightning of different polarities are discussed.
Similarity-Based Browsing of Image Search Results

In this demo paper, we present an image browsing system that is suitable for online visualisation and browsing of search results from Google Images. Our approach is based on the Huffman tables available in the JPEG headers of Google Images thumbnails. Since these are adapted to the images, we employ them directly as image features. We then generate a visualisation of the search results by projection onto a 2-dimensional visualisation space based on principal component analysis derived from the Huffman entries. Images are dynamically placed into a grid structure and organised in a tree-like hierarchy for visual browsing. Since we utilise information only from the JPEG header, the requirements in terms of bandwidth are low, while no explicit feature calculation needs to be performed, thus allowing for interactive browsing of online image search results.
Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.
An evaluation of image enhancement techniques for capillary imaging

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold, and is known to be effective particularly for early detection of scleroderma spectrum disorders and evaluation of Raynaud's phenomenon. Manual inspection of NC images can be aided by a computerised system avoiding the inherent ambiguity present in human judgment and improving the diagnosis speed. For such an automated analysis, image enhancement is typically the first step. The performance of an employed image enhancement algorithm is crucial due to its influence on subsequent algorithms. In this paper, we aim to provide a comparative evaluation of different image enhancement techniques for nailfold capillaroscopy (NC) images. In particular, we evaluate the performance of ten image enhancement/noise removal techniques for NC images as a pre-cursor to edge detection aimed at identifying capillaries. Results on a variety of NC images show that bilateral filters and enhancers, non local means and anisotropic diffusion provide the best image quality for this task.
JPEG2000 vs. JPEG from an image retrieval point of view

It is well known that JPEG2000 has a variety of advantages over its predecessor JPEG. JPEG2000 not only allows images to be coded with clearly better visual image quality, it also addresses a series of other issues. Unification of lossless and lossy compression modes, robustness to bit-errors to allow image transmission over noisy channels and provision of regions of interest (ROI) are only some of those that have been incorporated into the new standard. In this paper we look at the JPEG2000 versus JPEG debate from an image retrieval standpoint. While it seems evident that image compression will have a negative effect on the performance of retrieval algorithms our aim is to provide quantitative results of how severe this performance drop would be for JPEG2000 compression in comparison to standard JPEG encoding. Our results show that while high compression causes problems for retrieval of JPEG images, the retrieval performance of JPEG2000 images is almost independent of compression ratio.
Visualising image databases

In this paper we explore different ways in which large collections of images can be visualised. We discuss the three principle visualisation techniques employed for this purpose, namely dimensionality reduced mappings, clustering-based visualisations and graph-based representations. Mapping-based techniques try to present the relationships between images described by high-dimensional features in a low-dimensional visualisation space. Clustered visualisations group similar images based on content, metadata or time stamp information, while in graph-based approaches links between images are exploited to arrive at an intuitive display of the dataset. We highlight advantages and disadvantages of the various approaches and emphasise the need for a benchmark which allows objective evaluation of these systems.
High Performance Medical Image Registration Using a Distributed Blackboard Architecture

A major drawback of medical image registration techniques is the performance bottleneck associated with similarity computation. Such bottlenecks limit registration applications in situations where fast execution times are required. In this paper a novel framework for high performance intensity-based medical image registration is presented. Geometric alignment of both reference and sensed images is achieved through a combination of scaling, translation, and rotation. Crucially, similarity computation is performed intelligently by knowledge sources (KSs) organised in a worker/manager model. The KSs work in parallel and communicate with each other by means of a distributed blackboard architecture. Partitioning of the blackboard is used to balance communication and processing workloads. The registration framework presented demonstrates the flexibility of the coarse-grained parallelism employed and shows how high performance medical image registration can be achieved with non-specialised architectures. Experimental results obtained during testing show that substantial speedups can be achieved