nils-herrmann/clean_scirepeval huggingface dataset:
Contrast enhancement in dermoscopy images by maximizing a histogram bimodality measure

Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is typically the first step in this analysis yet is often limited by the quality of the images to be analyzed. In this paper, we present an effective method to enhance the contrast in dermoscopy images. Given an input RGB image, we determine the optimal weights to convert it to grayscale by maximizing a histogram bimodality measure. Experiments on a large set of images demonstrate that this adaptive optimization scheme increases the contrast between the lesion and the background skin, and leads to a more accurate separation of the two regions using Otsu's thresholding method.
Osteophyte detection for hand osteoarthritis identification in x-ray images using CNNs

In this paper we describe a class of analog algorithms based on the concept of cellular neural networks (CNNs) for detecting osteoarthritis (OA) from x-ray images. The indicator of OA that we examine is the presence of bony spurs or osteophytes in the vicinity of the weight bearing joints of the fingers. Results on a series of hand x-ray images are shown to be promising.
Parallel implementation of three-dimensional model of two-phase fluid filtration based on improved alternating triangular method

In this paper, we consider the Buckley-Leverett model for space-dimensional problems of two-phase filtration of incompressible fluids based on gravity and without capillary forces. An advanced modified alternating triangular iterative method (MATM) that takes into account the availability of sources (sinks) in a relatively small number of nodes characterised by generalised delta-shaped functions is applied for a discrete model that approximates the boundary-initial task of filtration. We investigate the numerical solution of computationally time-consuming tasks in the reservoir with significant heterogeneity of permeability, which changes by four orders of magnitude within the reservoir and has a significant advantage to achieve a given accuracy within the number of iterations. In order to develop a coherent version of the advanced MATM, a parallel algorithm of its numerical implementation, based on decomposition in two spatial directions, is implemented on a multi-processor system with shared memory containing 2048 cores and a peak performance of 18.8 teraflops. Compared with a traditional implementation, a significant efficiency boost is demonstrated leading to a tenfold reduction of the the simulation process.
An improved gradient vector flow algorithm for optic disc segmentation

The optic disc provides important cues for accurate diagnosis of various retinopathic diseases. Accurate segmentation of the optic disc is therefore an important step in the analysis of retinal images. Gradient vector flow (GVF) based segmentation algorithms have been used successfully on a variety of medical imagery, however, due to the compromise of internal and external energy forces, it can lead to less accurate segmentation in certain cases. In this paper, we show, that through incorporation of a mean shift term into the GVF framework, improved segmentation accuracy can be achieved. Experimental results on a large dataset of retinal images demonstrate that the presented method reliably detects the border of the optic disc.
Performance evaluation of a two-stage clustering technique for time-series data

This paper proposes a two-stage clustering technique for time-series data. The proposed method comprises two clustering procedures. First, the original time-series data are divided into subsequences to produce an initial clustering. The resulting group of clusters can be used as features that represent the time-series data. Then, the time-series data are converted into numerical vectors using the features generated by the first clustering stage. Finally, the converted numerical vectors undergo a second clustering procedure that produces the final clustering results. An extensive series of computational experiments are conducted in order to examine the performance of the proposed method.
Skin lesion extraction in dermoscopic images based on colour enhancement and iterative segmentation

Accurate extraction of lesion borders is a crucial step in analysing dermoscopic skin lesion images. In this paper we present an effective approach to extracting lesion areas by combining an iterative segmentation algorithm with a preprocessing step that enhances colour information and image contrast. Following the pre-processing, analysis of the image background is conducted by iterative measurements based on median and standard deviation of non-lesion pixels, which in turn facilitates automatic and recurring noise reduction and enhancement. The algorithm does not depend on the use of rigid threshold values as an optimal thresholding algorithm is used to determine the optimal threshold iteratively. Extensive experimental evaluation is carried out on a dataset of 90 dermoscopy images with known ground truths obtained from three expert dermatologists. The results show that our approach is capable of providing good segmentation performance and that the colour enhancement step is indeed crucial as demonstrated by comparison with results obtained from the original RGB images.
Classification of melanocytic skin lesions from non-melanocytic lesions

In this paper, we present a classification method of dermoscopy images between melanocytic skin lesions (MSLs) and non-melanocytic skin lesions (NoMSLs). The motivation of this research is to develop a pre-processor of an automated melanoma screening system. Since NoMSLs have a wide variety of shapes and their border is often ambiguous, we developed a new tumor area extraction algorithm to account for these difficulties. We confirmed that this algorithm is capable of handling different dermoscopy images not only those of NoMSLs but also MSLs as well. We determined the tumor area from the image using this new algorithm, calculated a total 428 features from each image, and built a linear classifier. We found only two image features, “the skewness of bright region in the tumor along its major axis” and “the difference between the average intensity in the peripheral part of the tumor and that in the normal skin area using the blue channel” were very efficient at classifying NoMSLs and MSLs. The detection accuracy of MSLs by our classifier using only the above mentioned image feature has a sensitivity of 98.0% and a specificity of 86.6% in a set of 107 non-melanocytic and 548 melanocytic dermoscopy images using a cross-validation test.
A novel approach to fault tolerant information and control system design

Design and construction of information and control systems (ICSs) for objects where failure would be critical is an important scientific problem. Such ICSs require both fault tolerance and survivability. Common methods and facilities for addressing fault tolerance do not use the capabilities of modern telecommunications equipment. In this paper, we present an integrated approach that allows to level up the fault-tolerance of an ICS, and requires new methods and algorithms for the reconfiguring procedure. The problem of defining the configuration is formalised, and results of an experimental study of the effectiveness of asynchronous parallel simulated annealing are presented and discussed.
Classifying HEp-2 cells in immunofluorescence images using multiple kernel learning

Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. Here, HEp-2 cells are categorised into different groups, which allow to make implications about different autoimmune diseases. Traditionally, this categorisation is performed manually by an expert and is hence both subjective and time intensive. In this paper, we present an effective method for classification of HEp-2 cells in which we first extract local binary pattern (LBP) texture features in form of multi-dimensional LBP (MD-LBP) histograms and then employ a multiple kernel learning approach to classification that integrates a multitude of support vector kernels generated by sampling the feature space. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate that our employed texture features are indeed useful for the differentiation of HEp-2 cells and that our multiple kernel learning based classification approach outperforms single kernel classification schemes. Our algorithm is shown to provide super performance compared to all techniques that were entered in the competition and to rival results obtained by a human expert.
Navigation and Browsing of Image Databases

Image collections are growing at an exponential rate and solutions to manage vast databases of images are hence highly sought after. Content-based image retrieval techniques have shown great potential, yet commonly employed approaches like query-by-example are only of limited usefulness. An interesting alternative is provided by systems that allow visual exploration of an image dataset through a browsing interface. In these methods the complete database, or parts thereof, is visualised through application of dimensionality reduction techniques, clustered visualisations or display of a graph structure. Once visualised, it should then be possible to browse through the collection in an interactive, intuitive and efficient way. In this paper we present various browsing techniques that can be employed for this purpose. Browsing can be achieved in several ways. We can distinguish between horizontal browsing which works on images of the visualisation plane, and includes operations such as panning, zooming, magnification and scaling, and vertical browsing which allows navigation to a different level of a hierarchically organised visualisation. Furthermore, browsing can also be accomplished by taking into account time stamp information, hence enabling temporal browsing. We conclude, highlighting the need for objective evaluation and benchmarking of browsing system and see one of the next research challenges in the development of effective image browsing tools for mobile devices.
An overview of Genetic Algorithms in simulation soccer

This paper discusses the use of genetic algorithms and genetic programming within the simulation soccer domain. Genetic algorithms (GAs) are based on the Darwinian theory of evolution and provide techniques to execute an effective search on a large range of potential solutions to a specific problem. Genetic Programming (GP) uses GA concepts to evolve a computer program. We show how GAs and GP have been applied to the challenging real-time and noisy domain of RoboCup simulation soccer. Among others, genetic approaches can be used to find appropriate actions for a soccer agent during a game, to improve different aspects of team strategy as well as to strengthen the ability of a player or a team in training exercises.
Retinal blood vessel segmentation using bee colony optimisation and pattern search

Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. In this paper, we propose an automated retinal blood vessel segmentation approach based on artificial bee colony optimisation in conjunction with fuzzy c-means clustering. Artificial bee colony optimisation is applied as a global search method to find cluster centers of the fuzzy c-means objective function. Vessels with small diameters appear distorted and hence cannot be correctly segmented at the first segmentation level due to confusion with nearby pixels. We employ a pattern search approach to optimisation in order to localise small vessels with a different fitness function. The proposed algorithm is tested on the publicly available DRIVE and STARE retinal image databases and confirmed to deliver performance that is comparable with state-of-the-art techniques in terms of accuracy, sensitivity and specificity.
An improved ensemble approach for imbalanced classification problems

Classification of imbalanced data is a challenging task in machine learning, as most classification approaches tend to bias towards the majority class, even though the minority class is often the one of greater importance. Consequently, methods that are capable of boosting the classification accuracy on the minority class are sought after. In this paper, we propose an improved ensemble approach for imbalanced classification. Our algorithm is based on undersampling of the majority class to create balanced object subspaces, on which individual classifiers are trained. As not all generated classifiers will be useful for the ensemble construction, we carry out a pruning procedure to discard irrelevant models. This classifier selection is based on a diversity measure to identify mutually complementary classifiers. The remaining predictors are combined using a trained fuser based on discriminants. Extensive experimental results on several benchmark datasets demonstrate our proposed method to adequately address class imbalance and to (statistically) outperform several state-of-the-art classifier ensembles dedicated to imbalanced classification.
Breast thermogram analysis using a cost-sensitive multiple classifier system

Breast cancer is the most commonly diagnosed form of cancer in woman. While mammography is recognised as the standard method of diagnosing breast cancer, it has been shown that thermography based cancer diagnosis is able to identify cancer patients earlier than mammography. Clearly, correctly diagnosis cancer patients is of higher importance than correctly recognising benign ones. Therefore in this paper, we present a cost-sensitive classification approach to breast cancer analysis from thermograms. We first extract a series of statistical image feature from the thermal images and utilise these in the classification process. Importantly, we use a multiple classifier system where the ensemble, consisting of cost-sensitive tree classifiers, is tuned using a genetic algorithm in such a way that misclassifying malignant cases carries a higher penalty/cost than misclassifying benign cases, resulting in a classification with significantly improved sensitivity. Our experimental results on a large dataset of breast thermograms with known ground truth demonstrate that our proposed approach gives excellent classification performance.
Remote QR code recognition based on HOG and SVM classifiers

QR codes have become useful and efficient data storage tools which are exploited in many commercial applications including product tracking, website redirection, etc. A QR code is a 2-dimensional barcode localised through three finder patterns (three squares characterised by a series of alternative black and white modules at ratios 1∶1∶3∶1∶1) placed in its three corners. QR codes are generally placed in different environments with complex backgrounds (overlapping text, pictures, etc.), and are often captured under unfavourable conditions such as poor lighting. These factors can significantly affect the recognition ability and thus may hinder correct QR code localisation and identification. In order to appropriately address these issues, in this paper, we present a QR code recognition algorithm based on histogram of oriented gradients (HOG) features combined with support vector machine (SVM) classifiers. Using HOG, we extract gradient features of each extracted pattern. Subsequently, the obtained features are passed to two linear SVM classifiers, one trained with finder patterns and one trained with alignment patterns, to remove irrelevant patterns. QR codes are then conveniently localised according to a pattern closeness constraint. In the last stage, the captured code is enhanced by applying a perspective correction followed by image binarisation and morphological processing. Finally, the patterns are decoded using an accurate 2-d barcode decoder. Our proposed approach is designed for an embedded systems using a Raspberry Pi equipped with a HD camera and a small robot carrying the equipment.
Wavelet based retrieval of medical thermograms

Thermography captures the temperature distribution of the human skin and is employed in various medical applications. Techniques for automaticaly retrieving medical images based on their content have shown to be useful and are highly sought after. In this paper we show that efficient and effective content-based retrieval of medical thermograms can be performed directly in the compressed domain of wavelet compressed images. Experimental results on a large dataset of medical infrared images confirm the usefulness of our presented approach.
Examining the Effect of Cost Assignment on the Performance of Cost-Based Classification Systems

This paper examines the performance of cost-based fuzzy classification systems for pattern classification problems with misclassification costs. The task here is to minimize the total sum of misclassification costs by fuzzy classification systems. In the cost-based fuzzy classification systems, the consequent class of a fuzzy if-then rule is determined so that the misclassification cost is minimized over the covered training patterns by the antecedent part of the fuzzy if-then rule. On the other hand, the conventional fuzzy classification systems are compatibility-based. That is, the consequent class of a fuzzy if-then rule is determined from the compatibility of training patterns covered by the antecedent part of the fuzzy if-then rule. The grade of certainty of the fuzzy if-then rules in both fuzzy classification systems is calculated by using the compatibility of training patterns from each class. In computational experiments, we compare the performance of the cost-based fuzzy classification systems with that of the conventional compatibility-based fuzzy classification systems.
A mean shift based fuzzy c-means algorithm for image segmentation

Image segmentation is an important task in many medical applications. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. C-means based approaches, in particular fuzzy c-means has been shown to work well for clustering based segmentation, however due to the iterative nature are also computationally complex. In this paper we introduce a new mean shift based fuzzy c-means algorithm that we show to be faster than previous techniques while providing good segmentation performance. The proposed clustering method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centres, the entire strategy is capable of optimally segmenting clusters within an image.
Neural network model of pumping units in oil preparation and pumping complex

Methods of improving the efficacy of oil extraction are highly sought after. One way to achieve such an improvement is to reduce the downtime of equipment and to implement measures to increase oil recovery for the entire life cycle of the well based on analysis of operational data. The work we present in this paper is aimed at improving a model of pumping units of the oil preparation and pumping complex. For this purpose, we employ an approach based on computational intelligence techniques and in particular an approach that is based on recurrent neural networks in combination with convolutional neural networks to address the problem of operative analysis of telemetric data from pumping units and to forecast the state of technological equipment. Our proposed approach provides an attractive model of optimizing the parameters of pumping equipment and thus a useful avenue of improving the efficacy of oil extraction.
Determining protrusion cephalometric readings from panoramic radiographic images

In the present investigation, positions of head landmarks in panoramic x-ray images in relation to cephalometric landmarks were studied. Special landmarks in right and left sides of mandible were manually identified and calibrated with reference readings. The measured landmarks are compared for both sides with different panoramic duration times. Results between left and right were compared using a thin-plate spline (TPS) geometric tool. The averages of both sides were reported and compared with frontal cephalometric readings. Differences between left and right landmarks were found to be significant in relation to cephalometric landmarks and differences between different duration times.
Introducing Class-Based Classification Priority in Fuzzy Rule-Based Classification Systems

In this paper we propose a fuzzy rule-generation method for pattern classification problems with classification priority. The assumption in this paper is that a classification priority is given a priori in relation to other classes. Our fuzzy rule-based classification system consists of a set of fuzzy if-then rules that are automatically generated from a set of given training patterns. The proposed method decides the consequent class of fuzzy if-then rules based on the number of covered training patterns for each class. In computational experiments we first show the effect of introducing classification priority for a synthetic two-dimensional problem. Then we show the effectiveness of the proposed method for several real-world pattern classification problems.
Fast JPEG image retrieval using tuned quantisation tables

In this paper, we present an extremely fast method for online image retrieval of JPEG compressed images. We exploit minimal perceptual error image compression which optimises JPEG quantisation tables to improve the resulting image quality. In particular, we demonstrate that thus tuned quantisation tables can be used as image descriptors for performing content-based image retrieval. Image similarity is expressed as similarity between the respective quantisation tables and feature extraction and comparison be performed in an extremely fast fashion as it is based on information only from the JPEG headers. We show that our method takes only about 2-2.5% of the time of standard compressed domain algorithms, yet achieves retrieval accuracy within 3.5% of these techniques on a large dataset.
An evaluation of classifier ensembles for class imbalance problems

Classification of imbalanced data represents a challenging task in machine learning, as most classification algorithms tend to bias towards the majority class, while often correctly identifying minority class instances is of greater importance. Consequently, there is a need for methods that provide improved accuracy for the minority class without sacrificing overall performance. Ensemble classification methods have been shown to be able to lead to both more robust as well as better performing classification approaches, while more recently, ensemble approaches have also been developed for imbalanced classification problems. In this paper, we evaluate seven state-of-the-art ensemble approaches and, in an extensive set of experiments, compare their performance on five benchmark datasets. Our results should allow to shed some light on strengths and weaknesses of the investigated algorithms.