nils-herrmann/clean_scirepeval huggingface dataset:
Look & listen: sonification and visualization of multiparameter micrographs

Multiparameter imaging techniques provide large numbers of high-dimensional image data in modern biomedical research. Besides algorithms for image registration, normalization and segmentation, new methods for interactive data exploration must be proposed and evaluated. We propose a new approach for auditory data representation, based on sonification. The approach is applied to a multiparameter image data set, generated with immunofluorescence techniques and compared to a conventional visualization approach and to a combination of both. For comparison, a psychophysical experiment was conducted, in which one standard evaluation procedure is modeled. Our results show, that all three approaches lead to comparable evaluation accuracies for all subjects. We conclude, that both, acoustical and visual approaches can be combined to display data sets of large dimensionality.
A machine learning based system for multichannel fluorescence analysis in pancreatic tissue bioimages

Fluorescence microscopy has regained much attention in the last years especially in the field of systems biology. It has been recognized as a rich source of information extending the existing sources since it allows simultaneous collection of spatial and temporal protein information. In order to enable a high-throughput and high-content image analysis, sophisticated image processing routines become essential. We present a machine learning based approach for semantic image annotation i.e. identifying biologically meaningful objects. A semantic annotation becomes necessary, if image variables have to be associated to single biological objects, for example cells. We apply our method to pancreatic tissue sample images to detect and annotate cells of the Islets of Langerhans and whole pancreas. Based on the annotation, aligned multichannel fluorescence images are evaluated for cell type classification allowing accurate and rapid determination of the cell number and mass. This high-throughput analytical technique, requiring only few parameters, should be of great value in diabetes studies and for screening of new anti-diabetes treatments.
Breast MRI data analysis by LLE

Locally linear embedding (LLE) has recently been proposed as a powerful algorithm for unsupervised learning and dimensional data reduction. For a first time we apply LLE to a problem of medical data analysis. Magnetic resonance imaging (MRI) is considered as an essential imaging modality in the detection and classification of breast cancer. In dynamic contrast enhanced MRI (DCE-MRI) the data set of each patient is composed of a sequence of images and each data point in the image is associated with one time-series feature vector. Our results show that LLE is capable of revealing the heterogeneity of malignant tumors from the data structure of DCE-MRI signals.
Analyzing multi-tag bioimages with BioIMAX colocation mining tools

The application of multi-tag protocols in fluorescence microscopy allows the visualization of a large number (>; 10) of molecules (i. e. proteins) in a sample (like a tissue section). However, the analysis of such high dimensional bioimages is a difficult task for most of the labs, since software solutions for particular data mining steps are difficult to use or just not available. In this paper we present two new free online tools: MICOLT (Multivariate Image COlocation Tool) and MIFIST (Multivariate Image Frequent Item Set Tool). Both tools can be used via our recently proposed online bioimage analysis platform BioIMAX, so users can upload their bioimage data, apply the tools and share the results with other invited users based on BioIMAX' concept of shared virtual projects. Data mining with these tools includes the computation and visualization colocation factors well established in the microscopy community (like Mander's score) and association rule mining following the frequent item set principle, thereby supporting large and small scale analysis.
Fluorescence micrograph segmentation by gestalt-based feature binding

We present the application of a recurrent neural network feature binding model to the segmentation of fluorescence micrographs, images showing fluorescent cells in tonsil tissue. Image primitives, referred to as features, consisting of position and local gradient information, build the input to the model. The competitive layer model is used to provide a binding of features to convex groups, corresponding to fluorescent cell bodies. Although the images contain noise, and the cells' shapes show considerable variation, the fluorescent cell contours are extracted with sufficient accuracy, according to a biomedical expert. The method achieves at the same time grouping and figure-ground segmentation, and does not require us to manually fix the number of groups.
COATL - a learning architecture for online real-time detection and classification assistance for environmental data

We propose a machine learning based approach to real-time detection and classification assistance for images from unknown environments. While systems for detecting and classifying regular structures like faces in still images are well established, the task of e. g. detecting new morphotypes/objects in an environment is much more complex. The morphotypes/objects are not guaranteed to have apriori known characteristics such as facial landmarks, thus adaptive and data-driven approaches are needed that are trained online with a growing number of available labels provided by a human observer. Our approach combines an effective points of interest computation with an online learning network for real-time proposition of classes. In an example application using images from an arctic deep-sea underwater observatory we report a maximum accuracy of 0.87 and a top-3 accuracy of 0.97 in the range from 10 to 1499 available labels. The average accuracy in that range is 0.74, with the top-3 accuracy being 0.93. Additionally, a convolutional neural network achieves an accuracy of 0.965 at 1500 labels. Points of interest are computed in approximately 3.7 seconds, right after loading a new image. Proposing a class for a computationally determined point of interest or a manually selected one takes about 0.2 seconds. If the image is already in cache the time is reduced to 0.1 seconds. The suggestion of points of interest combined with a proposition of putative classes allows for significantly faster labeling times in new environments.
Investigation of hidden parameters influencing the automated object detection in images from the deep seafloor of the HAUSGARTEN observatory

Detecting objects in underwater image sequences and video frames automatically, requires the application of selected algorithms in consecutive steps. Most of these algorithms are controlled by a set of parameters, which need to be calibrated for an optimal detection result. Those parameters determine the effectivity and efficiency of an algorithm and their impact is usually well known. There are however further non-algorithmic impact factors (or hidden parameters), which bias the training of a machine learning system as well as the subsequent detection process and thus need to be well understood and taken into account. In the context of megafauna detection in benthic images, we investigate the effects of some of these parameters on our machine learning based detection system iSIS. The images to be analyzed were taken at the deep-sea, long-term observatory HAUSGARTEN in which five experts labeled seven distinct object classes as an annotation gold standard. We found, that the hidden parameters from imaging as well as the fusion of expert knowledge could partly be compensated and were able to achieve detection performances of 0.67 precision and 0.87 recall. Despite the efforts to compensate the hidden parameters, the detection performance was still varying across the image transect. This poses the potential occurrence of further hidden parameters not taken into account so far.
Data-Driven Long Term Change Analysis in Marine Observatory Image Streams

In recent years, a number of fixed long-term underwater observatories (FUO) have been deployed to monitor marine habitats over time. HD cameras deployed on FUOs enable vision based studies of long-term processes in the monitored habitats. However, in many marine environments there is often only little a-priori knowledge about potential changes that can be expected or where such changes are likely to occur. Therefore, we propose a method to detect regions of potentially relevant changes and to group them into categories. Wavelet analysis is employed to extract features that describe the approximate progression of pixel values over time. Clustering the features using the recently proposed Bi-Domain Feature Clustering (BDFC) achieves feature grouping and a data-driven definition of change categories. Moreover, a relevance score is computed for each change category, to find regions with relevant changes and to illustrate different relevant change categories simultaneously in one image. Our experiments with images from the Lofoten Vesterålen (LoVe) ocean observatory demonstrate the effectiveness of the method to find relevant change patterns and associate them to different regions or biota.
Active Learning for the Classification of Species in Underwater Images from a Fixed Observatory

Vision based wildlife monitoring is an important task in the field of environmental monitoring. Wildlife monitoring activities often create large collections of data needing computational approaches to (semi-) automated detection and annotation of objects in the images/video. In this work, we consider the special case of marine wildlife monitoring using camera equipped fixed observatories. In such cases where a-priori knowledge about which species to find is limited, a standard computer vision approach, employing supervised learning, will not be applicable for detecting and classifying species (or events) in the images. In a recently proposed unsupervised learning method, image patches are extracted from a time series of underwater images that feature moving species (like starfish, etc). The patches are automatically grouped into clusters with similar morphology and a so called relevance score is assigned to each of the clusters describing the likeliness that it contains patches showing unusual changes. However, due to the unsupervised fashion (i) the categories don't have labels and (ii) do not reflect the species distribution satisfactory. In this paper, we propose an active learning method that builds upon these results and can be used to assign taxonomic categories to single patches based on a set of human expert annotations making use of the cluster structure and relevance scores. The evaluation shows that compared to traditional sampling strategies our approach uses significantly less manual labels to train a classifier. We are confident that the results are relevant for non-marine contexts as well.
A MeSH term based distance measure for document retrieval and labeling assistance

For biomedical and pharmaceutical research, the PUBMED database of the NLM (National Library of Medicine) has become a viable platform. It provides the means for profound investigations of past and related research in daily scientific work. One basic aspect is the search for articles related to a certain research topic. In order to express relatedness many text-mining or document retrieval approaches make use of the "bag of words" model in which unstructured text is represented as a vector of word counts. Since full length articles are not commonly available, many systems generate feature vectors from abstract data only - therefore limiting the explanatory power of their feature space. Since MeSH (Medical Subject Headings) assigned by human experts cover full length articles, we propose for the first time a nonEuclidean document distance measure based on MeSH tree structures. We quantitatively evaluate the approach in comparison to a standard vector space approach and a hybrid version of both. The MeSH-based showed promising results, yet it is still surpassed by the vector space model.
Computational coral feature monitoring for the fixed underwater observatory LoVe

Fixed underwater observatories (FUOs) equipped with a variety of sensors including HD cameras, allow long-term monitoring with a high temporal resolution of a limited area of interest. FUOs enable in situ monitoring of visual features like size and color of for instance live cold-water corals using imaging techniques. We present a computational workflow to extract coral features from the huge collection of images recorded by the FUO LoVe (Lofoten - Vesterålen)1. The presented approach allows to represent the image-time-series as numerical values of the features. This enables the images to be subject to statistical analysis to gain insight into the short- and long-term relationships between the corals and the environment. The presented automatic extraction of features from digital photos includes a customized pre-processing of the images (spatial and signal alignment), automated segmentation of the living parts of the corals using an unsupervised learning algorithm and extraction of coral specific numerical features which compensate color shifts due to changes of the in-optical properties affecting the whole image. In this initial study, unexpected temporal change patterns could be revealed, by comparing change patterns of live coral areas with change patterns of coral rubble areas using the CIELab color space.
A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections

A neural cell detection system (NCDS) for the automatic quantitation of fluorescent lymphocytes in tissue sections is presented in this paper. The system acquires visual knowledge from a set of training cell-image patches selected by a user. The trained system evaluates an image in 2 min calculating: the number, the positions, and the phenotypes of the fluorescent cells. For validation, the NCDS learning performance was tested by cross validation on digitized images of tissue sections obtained from inherently different types of tissue: diagnostic tissue sections across the human tonsil and across an inflammatory lymphocyte infiltrate of the human skeletal muscle. The NCDS detection results were compared with detection results from biomedical experts and were visually evaluated by our most experienced biomedical expert. Although the micrographs were noisy and the fluorescent cells varied in shape and size, the NCDS detected a minimum of 95% of the cells. In contrast, the cellular counts based on visual cell recognition of the experts were inconsistent and largely unreproducible for approximately 80% of the lymphocytes present in a visual field.
Change detection in marine observatory image streams using Bi-Domain Feature Clustering

Vision based environmental monitoring using fixed cameras generates large image collections, creating a bottleneck in data analysis. In areas with limited background knowledge of the monitored habitat, this bottleneck can often not be overcome by traditional pattern recognition methods. A new change detection method to identify interesting events such as presence and behavior of different species is proposed. The change detection method uses the new Bi-Domain Feature Clustering (BDFC). BDFC integrates the location of a feature vector in the feature space as well as the location in the image into the clustering. Firstly, BDFC is applied to a time dependent representation of the image stream to identify regions of similar change. Secondly it is applied to a time independent representation to group these changes into categories. These categories can rapidly be assessed by a human observer to bypass the time consuming inspection of the whole data set. To make the posterior browsing of detected changes more efficient, a relevance factor computed for each category is proposed. The approach is demonstrated with experimental runs, using images from the Lofoten Vesterålen ocean observatory, showing the potential to harvest changes of interest and novelties in large image collections.
Seabed Classification Using a Bag-of-Prototypes Feature Representation

The increasing scientific and economic interest in the visual exploration and monitoring of marine areas is creating huge amounts of new underwater image and video data and approaches to computationally assisted analysis are desperately needed. In this paper we propose an image patch feature representation concept, the Bag of Prototypes (BoP), to cope with the individual problems in underwater image analysis. We consider the case of seafloor classification, which is relevant in many contexts such as habitat mapping or the exploration of mineral resources and show, that the BoP concept allows an efficient and accurate tile-wise estimation of poly-metallic nodule coverage in relation to two differently acquired gold standards.
Detection of suspicious lesions in dynamic contrast enhanced MRI data

Dynamic contrast-enhanced magnet resonance imaging (DCE-MRI) has become an important source of information to aid breast cancer diagnosis. Nevertheless, next to the temporal sequence of 3D volume data from the DCE-MRI technique, the radiologist commonly adducts information from other modalities for his final diagnosis. Thus, the diagnosis process is time consuming and tools are required to support the human expert. We investigate an automatic approach that detects the location and delineates the extent of suspicious masses in multi-temporal DCE-MRI data sets. It applies the state-of-the-art support vector machine algorithm to the classification of the short-time series associated with each voxel. The ROC analysis shows an increased specificity in contrast to standard evaluations techniques.
Real-time interactive image segmentation using improved superpixels

Since it is very hard to use the conventional segmentation methods to extract an object with a variety of colors, interactive segmentation plays a more and more important role in image processing and is widely used in electronic products. However, performing interactive segmentation pixel by pixel always requires a lot of time. To improve the efficiency, in this paper, superpixels are applied instead of the original pixels for interactive segmentation. Moreover, since the accuracy may be reduced if the superpixel is applied, we also propose several ways to refine the superpixel. We use the variance to decide whether the superpixel should be refined. If so, the superpixel is re-segmented according to the color values of the adjacent superpixels. With the refinement, the edges of superpixels will highly match the boundaries of objects. Simulations show that using the refined superpixel instead of the original superpixel can achieve much better performance for interactive segmentation. Moreover, since the proposed algorithm is superpixel-based and the superpixels can be determined when users are drawing the lines, the computation time is much less than that of other interactive segmentation methods.
Color Images Enhancement using Weighted Histogram Separation

This paper presents a modified approach to the successive mean quantization transform, which is called as the weighted histogram separation (WHS) for enhancement of color images. Property of WHS situates between the successive mean quantization transform and the histogram equalization. In addition, this approach is further applied to the local enhancement, which is similar to the adaptive histogram equalization, and it is termed as the adaptive weighted histogram separation (AWHS). A comparison with successive mean quantization transform and histogram equalization has been performed in the experiments.
Reducing sampling error by prolate spheroidal wave functions and fractional Fourier transform

It is known that one can use Shannon's theory to sample a bandlimited signal. In this paper, we introduce how to use prolate spheroidal wave functions (PSWFs) to sample a time-limited and nearly band-limited signal. PSWFs have the property of optimal energy concentration. Thus we can apply it to sampling theory to reduce the aliasing error of the recovered signal. We derive a theory that can estimate the upper bound of the error. With it, we can determine, to achieve certain accuracy, how many samples we should acquire. Moreover, we combine the proposed sampling theory with the fractional Fourier transform (FRFT). We also find an important theory, i.e., to achieve a certain degree of accuracy, the number of sampling points required for a signal is proportional to the 'area' of its time-frequency distribution.
Efficient discrete fractional Hirschman optimal transform and its application

All of the existing N-point discrete fractional signal transforms require O(N2) computation complexity. In this paper, we propose a new discrete fractional signal transform whose computation complexity can be reduced to O(N1.5). This new transform is a fractional version of a DFT-based signal transform called as the Hirschman optimal transform (HOT) in the literature. Eigenvalues and eigenvectors properties of the HOT are also developed. Moreover, the proposed discrete fractional HOT transform is extended to further reduce the required computation complexity to linear order O(N). As an application example, we apply this new computationally efficient discrete fractional signal transform to encrypt digital images.
Adaptive preprocessing and combination techniques for light field image rendering

Image rendering is to construct a large image from several smaller images. It is very important for stereo image generating and light filed image processing. In this paper, we propose several techniques to improve the performance of image rendering. When perform disparity estimation, higher weights are assigned for edge and corner pixels to increase their effects. When perform linear combination, the weight functions with different fractional powers are assigned for different layers. Moreover, the adaptive disparity estimation mechanism is also applied for image rendering. Simulations show that, with these techniques the performance of image rendering is much better that of conventional methods.
Fractional, canonical, and simplified fractional cosine transforms

The Fourier transform can be generalized into the fractional Fourier transform (FRFT), linear canonical transform (LCT), and simplified fractional Fourier transform (SFRFT). They extend the utilities of original Fourier transform, and can solve many problems that can not be solved well by original Fourier transform. We generalize the cosine transform. We derive the fractional cosine transform (FRCT), canonical cosine transform (CCT), and simplified fractional cosine transform (SFRCT). We show that they are very similar to the FRFT, LCT, and SFRFT, but they are much more efficient for dealing with the even, real even functions. For digital implementation, FRCT and CCT can save 1/2 of the real number multiplications, and SFRCT can save 3/4. We also discuss their applications, such as optical system analysis and space-variant pattern recognition.
Efficient DC term encoding scheme based on double prediction algorithms and Pareto probability models

In this paper, a new algorithm which adopts the techniques of double prediction and the Pareto probability model was applied to encode the DC term in the JPEG compression process. Conventionally, the DC term was encoded by differential coding, i.e., the difference of the DC values between the current block and the previous block. In this paper, we first use the DC terms of four adjacent blocks to predict the current DC value. We then further use the prediction error of the four adjacent blocks to estimate the variance of the prediction error of the current block. We call it the double prediction algorithm. Next, the Pareto distribution is applied to model the probability distribution of the prediction error. Simulation results show that, with the proposed algorithms, the data size required for DC terms is significantly reduced by 25% ~ 60% and a much higher compression rate can be achieved.