nils-herrmann/clean_scirepeval huggingface dataset:
Reversion Correction and Regularized Random Walk Ranking for Saliency Detection

In recent saliency detection research, many graph-based algorithms have applied boundary priors as background queries, which may generate completely “reversed” saliency maps if the salient objects are on the image boundaries. Moreover, these algorithms usually depend heavily on pre-processed superpixel segmentation, which may lead to notable degradation in image detail features. In this paper, a novel saliency detection method is proposed to overcome the above issues. First, we propose a saliency reversion correction process, which locates and removes the boundary-adjacent foreground superpixels, and thereby increases the accuracy and robustness of the boundary prior-based saliency estimations. Second, we propose a regularized random walk ranking model, which introduces prior saliency estimation to every pixel in the image by taking both region and pixel image features into account, thus leading to pixel-detailed and superpixel-independent saliency maps. Experiments are conducted on four well-recognized data sets; the results indicate the superiority of our proposed method against 14 state-of-the-art methods, and demonstrate its general extensibility as a saliency optimization algorithm. We further evaluate our method on a new data set comprised of images that we define as boundary adjacent object saliency, on which our method performs better than the comparison methods.
Augmenting intensity to enhance scene structure in prosthetic vision

We present a novel visual representation for prosthetic vision that augments intensity in order to emphasise regions of structural change. This is achieved via the adaptation of a recently proposed method for measuring the extent of local variation of surface orientation in corresponding disparity images. The proposed visual representation demonstrates how intensity and depth data may be combined to provide a scene representation that shows visual appearance as brightness in the familiar way (i.e., intensity-based), but ensures structurally important features such as steps, doorways and drop-offs, as well as general items of interest remain perceivable, regardless of contrast. Qualitative comparisons of the proposed visual representation in simulated prosthetic vision (98 phosphenes) suggest potential advantages over nonaugmented intensity for distinguishing between free and obstructed space in the scene, and for perceiving features of interest on smooth surfaces.
A Locally Constrained Random Walk Approach for Airway Segmentation of Low-Contrast Computed Tomography (CT) Image

Positron emission tomography (PET) combined with computed tomography (CT) is a routine imaging modality for the diagnosis and interpretation of malignant diseases of the thorax. Accurate airway segmentation is critical for the localization of sites of abnormal metabolism detected with PET-CT. The vast majority of published segmentation algorithms, however, are designed for high- resolution CT and these algorithms do not perform well with the low-contrast CT acquired in PET-CT images. In this study, we present a new fully automated airway segmentation algorithm that is optimised to tolerate the image characteristics inherent in low-contrast CT images. Our algorithm accurately and robustly segments the airway by introducing: (i) a robust multi-atlas initialisation which incorporates shape priori knowledge for seeds derivation; and (ii) a modified knowledge-based random walk segmentation that uses the derived seeds and manipulates the weights of the edge paths in a locally constrained search space. Our proposed algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance in segmentation results against comparative state-of- the-art algorithms.
A web-based medical multimedia visualisation interface for personal health records

The healthcare industry has begun to utilise web-based systems and cloud computing infrastructure to develop an increasing array of online personal health record (PHR) systems. Although these systems provide the technical capacity to store and retrieve medical data in various multimedia formats, including images, videos, voice, and text, individual patient use remains limited by the lack of intuitive data representation and visualisation techniques. As such, further research is necessary to better visualise and present these records, in ways that make the complex medical data more intuitive. In this study, we present a web-based PHR visualisation system, called the 3D medical graphical avatar (MGA), which was designed to explore web-based delivery of a wide array of medical data types including multi-dimensional medical images; medical videos; text-based data; and spatial annotations. Mapping information was extracted from each of the data types and was used to embed spatial and textual annotations, such as regions of interest (ROIs) and time-based video annotations. Our MGA itself is built from clinical patient imaging studies, when available. We have taken advantage of the emerging web technologies of HTML5 and WebGL to make our application available to a wider base of users and devices. We analysed the performance of our proof-of-concept prototype system on mobile and desktop consumer devices. Our initial experiments indicate that our system can render the medical data in a fashion that enables interactive navigation of the MGA.
Automatic medical image categorization and annotation using LBP and MPEG-7 edge histograms

This paper presents a global and local texture features combination as the description of medical images. The approaches use the block based Local Binary Pattern (LBP) texture histogram and MPEG-7 edge histogram together as the medical image description. The block histograms and the whole image texture histograms are accumulated to generate the image feature vector. Then, One-against-One SVM is adopted for automatic medical image annotation task in ImageCLEFmed2007. The experiments show that Local Binary Pattern texture and Edge Histogram are powerful for automatic medical image annotation.
Medical Image Registration via Steerable Pyramid

Image registration is enabling in integrating complementary and heterogeneous information from multiple images, and is particularly important for high-quality healthcare. To improve registration efficiency and accuracy, in this paper, a two-resolution-scale registration approach is proposed. Firstly, to speed up calculation, the images will be decomposed into multi-scale and multi-band representation by steerable pyramid that outweighs wavelets by providing invariance for both translation and rotation. Then, to avoid transformation error accumulation and magnification during the parameter transmission in the traditional multi-scale registration, the registration will be performed only in the lowest-resolution scale and the highest-resolution scale. In the former scale, the global rotation and scaling parameters will be calculated rapidly and accurately, which then will be directly used to initialize optimization in the latter scale, where, the translation differences will be corrected. The experiments on medical images demonstrate that the proposed registration is of good performance.
Broad and flat emission spectrum from InGaAs/InGaAlAs asymmetric multiple quantum wells: effect of well-width and modulation-doping profiles

Asymmetric InGaAs/InGaAlAs multi-quantum wells exhibit a = 1.55 mum broad and flat emission spectrum with 3-dB bandwidth up to 2500 Aring. Emission spectra and laser diodes by the effects of well-width and modulation-doping profiles are presented
Feature-Based Image Patch Approximation for Lung Tissue Classification

In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.
Hierarchical elastic registration of human brain images based on wavelet decomposition

Wavelets have been studied and applied in the multimedia and video processing community, including some areas in medical imaging, however, wavelet-based medical image registration has not been explored properly. In this paper, we propose an hierarchical registration method based on wavelet decomposition. Firstly, based on the wavelets, the images are decomposed into subbands which compose the registration pyramid; then, in each hierarchy, the affine registration based on mutual information is performed and the results of the current registration are used as the initial guess for the next hierarchy; finally, to further improve the registration performance, the local elastic registration is carried out. The proposed algorithm has been validated by experiments on clinical tomographic images and our experimental results demonstrate the the proposed method is of efficiency and good accuracy.
The accurate extraction and tracking of moving objects for video surveillance

In this paper, an accurate extraction approach and an effective tracking algorithm for moving objects are proposed. We first describe our extraction algorithm, which can extract objects precisely by using the mixed differences between adaptive background estimation and the two adjacent frames. Then we design a tracking algorithm, in which we use several IIR filters to predict the moving objects position and improve efficiency of the objects matching by using the moving objects temporal image and extracting results. The proposed tracking algorithm can track the objects accurately even if the objects are in the process of acceleration and have slight deformations. The efficiency of the proposed algorithms is demonstrated by several experiments given in this paper.
Automated Detection of the Occurrence and Changes of Hot-Spots in Intro-subject FDG-PET Images from Combined PET-CT Scanners

Dual-modality PET-CT imaging has been prevalently used as an essential diagnostic tool for monitoring treatment response in malignant disease patients. However, evaluation of treatment outcomes in serial scans by visual inspecting multiple PET-CT volumes is time consuming and laborious. In this paper, we propose an automated algorithm to detect the occurrence and changes of hot-spots in intro-subject FDG-PET images from combined PET-CT scanners. In this algorithm, multiple CT images of the same subject are aligned by using an affine transformation, and the estimated transformation is then used to align the corresponding PET images into the same coordinate system. Hot-spots are identified using thresholding and region growing with parameters determined specifically for different body parts. The changes of the detected hot-spots over time are analysed and presented. Our results in 19 clinical PET-CT studies demonstrate that the proposed algorithm has a good performance.
A bag-of-importance model for video summarization

In this paper, we propose a novel local feature based approach, namely Bag-of-Importance (BoI) model, for static video summarization, while most of the existing approaches characterize each video frame with global features to derive the importance of each frame. Since local features such as interest points are more discriminative in characterizing visual content, we formulate static video summarization as a problem of identifying representative frames which contain more important local features, where the representativeness of each frame is the aggregation of the importance of the local features contained in the frame. In order to derive the importance of each local feature for a given video, we employ sparse coding to project each local feature into a sparse space, calculate the l2 norm of the sparse coefficients for each local feature, and generate the BoI representation with the distribution of the importance over all the local features in the video. We further take the perceptual difference among spatial regions of a frame into account, a spatial weighting template is utilized to differentiate the importance of local features for the individual frames. With the proposed video summarization scheme, both the inter-frame and intra-frame properties of local features are exploited, which allows the selected frames capture both the dominant content and discriminative details within a video. Experimental results on a dataset across several genres demonstrate that the proposed approach clearly outperforms the state-of-the-art method.
A method of keyframe setting in video coding: fast adaptive dynamic keyframe selecting

In this paper, we describe a novel method of keyframe setting in video coding: fast adaptive keyframe selecting. The method analyzes video contents and decides keyframe setting dynamically and adaptively. Utilizing information generated by motion-estimation, our method involves only a little additional computing cost. Our method can improve both objective and subjective quality compared with traditional keyframe setting. It can be applied to any motion-estimation based video compression. We tested the method on not only standard test sequences but also real movie sequences proving that it gives valuable results.
Iterative keyframe selection by orthogonal subspace projection

Recent developments on sparse dictionary selection have demonstrated promising results for Video Summarization (VS). However, the convex relaxation based solution cannot ensure the sparsity of the dictionary directly. In this paper, a selection matrix is proposed to model the VS problem, according to which the L0 norm of this selection matrix is imposed to ensure sparsity directly. As a result, a computational efficient Orthogonal Subspace Projection (OSP) based Iterative Keyframe Selection (IKS) algorithm is proposed for VS. In addition, a Percentage Of Reconstruction (POR) criterion is proposed to provide an intuitive and flexible control of the length of final video summaries even without prior knowledge of a given video. Experimental results on a popular benchmark dataset demonstrate that our proposed algorithm outperforms the state-of-the-art methods.
Performance evaluation of functional medical imaging compression via optimal sampling schedule designs and cluster analysis

In previous work we have described a technique for the compression of positron emission tomography (PET) image data in the spatial and temporal domains based on optimal sampling schedule designs (OSS) and cluster analysis. It can potentially achieve a high data compression ratio greater than 80:1. However, the number of distinguishable cluster groups in dynamic PET image data is a critical issue for this algorithm that has not been experimentally analyzed on clinical data. In this paper, the problem of experimentally determining the ideal cluster number for the algorithm for PET brain data is addressed.
Real-time moving object detection under complex background

Moving object detection (MOD) is a basic and important problem in video analysis and vision applications. In this paper, a novel MOD method is proposed using global motion estimation and edge information. In order to get more robust MOD results under different backgrounds and lighting conditions, a bilinear model and histogram scaling method are used respectively for spatial and illumination normalization. After normalization, edges are extracted by Canny and further filtered using morphological operators to get closed object contours. The final objects are extracted by combining the contours and moving regions from motion detection. The experimental results show the proposed approach has apparent advantages in robust and accurate detection and tracking of moving objects with changing of camera positions, lighting conditions and background for real-time applications.
Automatic Preview Frame Selection for Online Videos

The preview frame of an online video plays a critical role for a user to quickly decide whether to watch the video. However, the preview frames of most online videos such as those shared on social media platforms are either selected heuristically (e.g., the first or middle frame of a video) or manually by users or experienced editors. In this paper, we investigate the challenging automatic preview frame selection task and formulae it as a classification problem. To our best knowledge, this is the one of the first attempts on this topic, since most existing key frame selection methods do not explicitly aim for selecting the best representative one only. Considering that a preview frame for an entire video should be informative in the context of the video story, attention catching, and of high visual quality, we propose three types of features to characterize each video frame: informativeness, attention, and aesthetics. Due to the imbalanced nature of training data (i.e., one preview frame only vs thousands of non-preview frames in a video), we utilize random forests to learn the features of preview frames and to classify each frame into preview frame or non-preview frame. In addition, we also increase the number of positive training samples by identifying frames which are visually similar to the preview frame. We evaluated our proposed method both quantitatively and qualitatively with a set of 180 news videos manually collected from the BBC news website. Experimental results indicate that our method is promising. We also investigated the contribution of each visual feature to guide future studies.
Optimal Dual Time Point for FDG-PET in the Differentiation of Benign from Malignant Lung Lesions: A Simulation Study

Dual time point imaging has been proposed as a means of improving the accuracy of Fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) in the diagnosis of lung malignancy. However, various sampling schedules have been used, which makes direct comparisons between their results difficult. It is unclear whether these schedules have the same accuracy for the diagnosis of lung malignancy, limiting the further development and adoption of these techniques. Although theoretically malignant and benign lesions show increasing difference in FDG uptake with longer uptake periods, the increasing noise due to decay may counteract this advantage and increase variability. In this paper, a method for the design of an optimal dual time point 18F-FDG PET imaging protocol has been proposed to address this issue.
Dual-scale medical image registration based on steerable wavelet

Image registration is indispensable to high-quality healthcare by integrating heterogeneous information in medical images captured from multiple imaging devices and at different imaging sessions. Multi-scale image registration has potential to achieve accelerated computation and to find global optimization through "coarse-to-fine" registration scheme. To retain these properties and meanwhile to avoid transformation error propagation in multi-scale registration, a dual-scale approach is proposed in this paper. Based on steerable wavelet with properties of translation- and rotation-invariance, the results of fast registration in the lowest resolution level are directly passed to initialize the registration in the highest resolution level. The experiments on medical images demonstrate that the proposed registration could be used for images with increased data volume.
A Wavelet-Based Reconstruction Algorithm for Fluorescent Molecular Tomography

Image reconstruction of fluorescent molecular tomography (FMT) involves repeatedly solving the large-dimension matrix equations which are computationally expensive, especially for the case when there are large deviations in the optical properties between the target and the reference medium. In this paper, a wavelet-based reconstruction approach is proposed for the FMT reconstruction in combination with a parallel forward computing strategy, in which both the forward and the inverse problem of FMT are solved in the wavelet domain. Simulation results demonstrate that the proposed approach can significantly speed up the reconstruction process and improve the image quality of FMT.
Supervised Variational Model With Statistical Inference and Its Application in Medical Image Segmentation

Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level set algorithms often assume piecewise constant or piecewise smooth for segments, which are implausible for general medical image segmentation. Furthermore, low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms. Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region density distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and background. The region-based statistical model in our algorithm can intuitively provide better performance on noisy images. We constructed a weighted probability map on graphs to incorporate spatial indications from user input with a contextual constraint based on the minimization of contextual graphs energy functional. We measured the performance of our approach on ten noisy synthetic images and 58 medical datasets with heterogeneous intensities and ill-defined boundaries and compared our technique to the Chan-Vese region-based level set model, the geodesic active contour model with distance regularization, and the random walker model. Our method consistently achieved the highest Dice similarity coefficient when compared to the other methods.
A deformable cosegmentation algorithm for brain MR images

Cosegmentation aims to simultaneously segment the common parts in a pair of images, and has recently attracted increasing research attention in the field of computer vision. In this paper, we propose a novel deformable cosegmentation (D-C) algorithm to solve the brain MR image segmentation problem by cosegmenting the image and a co-registered atlas. In this manner, the prior heuristic information about brain anatomy that is embedded in the atlas can be transformed into the constraints that control the segmentation of brain MR images. Based on the multiphase Chan-Vese model, the proposed D-C algorithm is implemented using level set techniques. Then, it is compared to the protocol algorithm and the state-of-the-art GA-EM algorithm in T1-weighted brain MR images corrupted by different levels of Gaussian noise and intensity non-uniformity. Our results show that the proposed D-C algorithm can differentiate major brain structures more accuratly and produce more robust segmentation of brain MR images.