nils-herrmann/clean_scirepeval huggingface dataset:
Multiple block based authentication watermarking for distribution of medical images

To provide an effective authentication method for distribution of medical images, we propose a new fragile watermarking method utilizing segmentation information of medical image contents. For medical image modalities like CT, MRI and PET, tissue structures contain a significant amount of clinical information. Hence, it is important to provide an authenticity check of the segmented blocks. The proposed method is based on cryptographically secure fragile watermarking but eliminates the problem of the block-wise independency of existing methods. The vector quantization (VQ) counterfeiting attack is a known vulnerability due to the block-wise independency of existing methods. In our approach, multiple signatures from two different types of blocks are used to defy such attacks. More secure distribution of medical images can be achieved by embedding the watermark.
Variational Bayes Inference Based Segmentation of Heterogeneous Lymphoma Volumes in Dual-Modality PET-CT Images

Accurate segmentation of heterogeneous carcinoma lesions in medical images is vital to the treatment planning, assessment of therapy response and other oncological applications. With current state-of-the-art imaging modalities, the CT images enhance the interpretation of cancer functional abnormalities. We applied the variational Bayes inference (VBI) model on both anatomical and functional information for delineating lesion boundary. The model is improved by clinical meaningful initialisation. Clinical data consisting of eight lesions with inhomogeneous carcinoma distribution were used to evaluate the model accuracy. Our algorithm is capable of isolating lesions from background with higher accuracy comparing to the wildly used threshold (40% of SUVmax). The VBI segmentation error is less than 6.11% ± 4.92% which is much better than the results performed by fixed threshold method. The experimental results show that our novel statistic method can produce more accurate segmentation of heterogeneous lymphoma volume in PET-CT images.
Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker

Prostate cancer is the second most common cause of cancer deaths in males. Accurate prostate segmentation from magnetic resonance (MR) images is critical to the diagnosis and treatment of prostate cancer. Automated prostate segmentation is challenging due to the variety in shapes and sizes of the prostate. Furthermore, the expected boundaries of ROIs are often indistinct, while heterogeneity concurrently exists within the ROIs. To address these challenges, we propose an automated approach that incorporates the local intensity features by random walker (RW) algorithm and global probability knowledge from an atlas to better describe unique characteristics of the prostate in MR images. We formulated a new RW weight function to take into account atlas probabilities and intensity differences. The prior knowledge from the atlas probability map not only reflects the statistical shape approximation of the prostate but also provides confinement and guidance for RW segmentation. Our approach was validated and compared with the conventional RW algorithm on segmenting 30 3-T prostate MR volumes. The experimental results indicated that our approach with an average DSC of 80.7±5.1%, outperformed that of the conventional RW (average DSC = 71.9±9.1%) and several other reported methods in terms of DSC accuracy and robustness.
Temporal Processing of dynamic positron emission tomography via principal component analysis in the sinogram domain

In this paper, we compare various temporal analysis schemes applied to dynamic PET for improved quantification, image quality and temporal compression purposes. We compare an optimal sampling schedule (OSS) design, principal component analysis (PCA) applied in the image domain, and principal component analysis applied in the sinogram domain; for region-of-interest quantification, sinogram-domain PCA is combined with the Huesman algorithm to quantify from the sinograms directly without requiring reconstruction of all PCA channels. Using a simulated phantom FDG brain study and three clinical studies, we evaluate the fidelity of the compressed data for estimation of local cerebral metabolic rate of glucose by a four-compartment model. Our results show that using a noise-normalized PCA in the sinogram domain gives similar compression ratio and quantitative accuracy to OSS, but with substantially better precision. These results indicate that sinogram-domain PCA for dynamic PET can be a useful preprocessing stage for PET compression and quantification applications.
An Intelligent Middleware for Dynamic Integration of Heterogeneous Health Care Applications

In order to accelerate health care information process, to improve patient safety and quality and benefits of healthcare, and to provide comprehensive electronic patient record, we propose an intelligent middleware with two-layer knowledge-based architecture, which can integrate heterogeneous health care applications dynamically. In the first data sources integration layer, standard-based health care ontology and global API metadata are achieved, which make authorized health care applications "understand" and operate heterogeneous data sources automatically and transparently. Based on this unified information, in the second application integration layer, configurable standard-based application workflow integration facilitates the communication and interoperation between the disparate health care applications. The proposed knowledge-based architecture can optimize standard-based medical data exchange, and improves the flexibility, interoperability, maintainability, scalability and extensibility of health care information systems.
Design of high capacity 3D print codes aiming for robustness to the PS channel and external distortions

The process of adding high-density information onto printed material enables and improves interesting hardcopy document applications, such as: security, authentication, physical-electronic round tripping, item-level tagging as well as consumer/product interaction. This investigation on robust and high capacity print codes aims to maximize information payload in a given printed page area, subject to robustness to distortions originated by printing and scanning processes and also to degradations introduced by user manipulation of printed documents. The novel approach includes statistical print-and-scan channel characterization, designing of robust segmentation, unsupervised Bayesian color classification with expectation-maximization algorithm for parameters estimation of a mixture of Gaussians model and design of error correction codes. Results illustrate the performance evaluated under real channel and distortions conditions. High payload is achieved with sufficient robustness to distortions resulting of regular office hardcopy document handling: print-and-scan channel and user manipulation.
Super-resolution of video using key frames and motion estimation

Many scalable video coding systems use frame down- sampling in order to reduce complexity and to enable enhancement layers. Super-resolution (SR) can be used to help the up-sampling and recovering processes of those frames. We are interested in reversed-complexity (distributed) coding methods, wherein few key frames are encoded at normal resolution, while the rest are down- sampled and encoded at reduced resolution along with the enhancement layers. We are only interested on the decoder side, wherein we carry motion estimation of the down- sampled frames using the key frames as references. When a match is made, the high-frequency components of the key frames (KF) are used to super-resolve the non-key frames (NKF). The motion estimation process is performed using blocks of band-pass versions of the frames, rather than low- pass ones. Results indicate the improved performance of the proposed super-resolution algorithm.
Rate-distortion analysis of weighted prediction for error resilience

In this paper, we extend our work in  and address an approach to theoretically analyze the rate-distortion (R-D) performance of the weighted prediction feature provided within the scope of H.264/AVC. We consider the weighted prediction as a standard-compatible leaky prediction approach for the purpose of error resilience. We adopt a quantization noise model that explicitly formulates the relationship between the data rate and the distortion in the mean-square-error (MSE) sense. We derive a comprehensive rate-distortion function for both the error- free scenario and the one with error drift. Through adjusting the weight coefficients in H.264/AVC, we also simulate H.264/AVC video streaming over error-prone networks and obtain the operational rate-distortion results using various leaky factors for both error-free and error- drift scenarios. We compare our theoretical results with the operational R-D curves and demonstrate that the theoretical results conform with the operational results.
A vector set partitioning noisy channel image coder with unequal error protection

A vector enhancement of Said and Pearlman's set partitioning in hierarchical trees (SPIHT) methodology, named VSPIHT, has been proposed for embedded wavelet image compression. A major advantage of vector-based embedded coding with fixed length VQs over scalar embedded coding is its superior robustness to noise. We show that vector set partitioning can effectively alter the balance of bits in the bit steam so that significantly fewer critical bits carrying significant information are transmitted, thereby improving inherent noise resilience. For low noise channels, the critical bits are protected, while the degradation in reconstruction quality caused by errors in noncritical quantization information, can be reduced by appropriate VQ indexing, or designing channel optimized VQs for the successive refinement systems. For very noisy channels, unequal error protection to the critical and noncritical bits with either block codes or convolution codes are used. Additionally, the error protection for the critical bits is changed from pass to pass. A buffering mechanism is used to produce an unequally protected bit stream without sacrificing the embedding property. Extensive simulation results are presented for noisy channels, including bursty channels.
Rate-distortion optimized mode selection for very low bit rate video coding and the emerging H.263 standard

This paper addresses the problem of encoder optimization in a macroblock-based multimode video compression system. An efficient solution is proposed in which, for a given image region, the optimum combination of macroblock modes and the associated mode parameters are jointly selected so as to minimize the overall distortion for a given bit-rate budget. Conditions for optimizing the encoder operation are derived within a rate-constrained product code framework using a Lagrangian formulation. The instantaneous rate of the encoder is controlled by a single Lagrange multiplier that makes the method amenable to mobile wireless networks with time-varying capacity. When rate and distortion dependencies are introduced between adjacent blocks (as is the case when the motion vectors are differentially encoded and/or overlapped block motion compensation is employed), the ensuing encoder complexity is surmounted using dynamic programming. Due to the generic nature of the algorithm, it can be successfully applied to the problem of encoder control in numerous video coding standards, including H.261, MPEG-1, and MPEG-2. Moreover, the strategy is especially relevant for very low bit rate coding over wireless communication channels where the low dimensionality of the images associated with these bit rates makes real-time implementation very feasible. Accordingly, in this paper, the method is successfully applied to the emerging H.263 video coding standard with excellent results at rates as low as 8.0 Kb per second. Direct comparisons with the H.263 test model, TMN5, demonstrate that gains in peak signal-to-noise ratios (PSNR) are achievable over a wide range of rates.
Receiver error concealment using acknowledge preview (RECAP) - An approach to resilient video streaming

High-quality and low-latency video streaming is essential to providing a natural user experience in video conferencing. This is challenging over lossy networks since compressed video is highly fragile while the low-latency requirement limits the effectiveness of traditional error control approaches such as retransmission and forward error correction. In this paper, we advocate a practical solution for low-latency video communications over best-effort networks that employs an additional low-quality, low-resolution but robustly coded copy of the video. This approach, called RECAP, incurs minimal rate overhead, and can be combined with previously decoded frames to achieve effective concealment of isolated and burst losses even under tight delay constraints. RECAP achieves PSNR gains of 2–6 dB against complete frame loss.
Vector SPIHT for embedded wavelet video and image coding

The set partitioning in hierarchical trees (SPIHT) approach for still-image compression proposed by Said and Pearlman (1996) is one of the most efficient embedded monochrome image compression schemes known to date. The algorithm relies on a very efficient scanning and bit-allocation scheme for quantizing the coefficients obtained by a wavelet decomposition of an image. In this paper, we adopt this approach to scan groups (vectors) of wavelet coefficients, and use successive refinement vector quantization (VQ) techniques with staggered bit-allocation to quantize the groups at once. The scheme is named vector SPIHT (VSPIHT). We present discussions on possible models for the distributions of the coefficient vectors, and show how trained classified tree-multistage VQ techniques can be used to efficiently quantize them. Extensive coding results comparing VSPIHT to scalar SPIHT in the mean-squared-error sense, are presented for monochrome images. VSPIHT is found to yield superior performance for most images, especially those with high detail content. The method is also applied to color video coding, where a partially scalable bitstream is generated. We present the coding results on QCIF sequences as compared against H.263.
Efficiency improvements for a geometric-partition-based video coder

H.264/AVC has brought an important increase in coding efficiency in comparison to previous video coding standards. One of its features is the use of macroblock partitioning in a tree-based structure. The use of macroblock partitions based in arbitrary line segments, like wedge partitions, has been reported to increase coding gains. The main problem of these non-standard new partitions schemes is the increase in computational complexity. Thus, our work proposes improvements in this extension to the H.264/AVC standard. First, we present a motion vector prediction scheme based on directional partitions. Second, we present a method for complexity reduction based on the most frequent partitions. The results show that it is possible to still produce good coding gains with lower complexity than previous approaches.
A pre-filtering approach to exploit decoupled prediction and transform block structures in video coding

Recent video coding techniques allow for decoupling of the transform block partition from that employed for prediction. For example, HEVC allows a transform block to overlap multiple prediction blocks. This paper is premised on the observation that in order to truly realize the potential of such enhanced flexibility, it is necessary to account for and mitigate considerable side effects due to stitching together independently predicted blocks, including the emergence of spurious high frequency components from sharp transitions across boundaries, which undermine the transform efficacy. The proposed solution involves an appropriately designed pre-filtering approach to mitigate boundary transition effects whenever a transform spans data from multiple prediction blocks. Moreover, this filtering technique enables extending the flexibility in decoupling prediction and transform structures, as various restrictions may now be eliminated. In particular, it makes it possible and beneficial to allow a transform block to span residual data from both inter and intra predicted blocks, whereas HEVC necessarily forces a single type of prediction in each coding unit. The method is further extended to include motion refinement that accounts for the pre-filtering approach. Experiments provide evidence for consistent coding gains over HEVC and VP9.
Nonlinear control of a benchmark beam balance experiment using variable hyperbolic bias

Magnetic bearing control schemes frequently use a bias current together with a superimposed control current to improve actuator gain and linearity. In this paper a variable bias scheme is developed to reduce electrical power loss while maintaining the control performance similar to conventional bias control. Two different bias schemes are proposed: a linear bias scheme and a hyperbolic bias scheme. A bias-dependent feedback linearization approach is applied to both systems to make the input/output relation of the system linear, and an observer-based stabilizing controller is designed to stabilize the nominal closed loop system. The resulting closed loop system dynamics are independent of bias except for amplifier saturation. The bias is minimized while avoiding amplifier saturation and singularity in feedback linearization with a separate bias control loop.
Vector set-partitioning with successive refinement Voronoi lattice VQ for embedded wavelet image coding

While lattice vector quantization (LVQ) can solve the complexity problem of LBG based vector quantizers, and also yield very general codebooks, a single stage lattice VQ, when applied to high variance vectors result in very large and unwieldy indices, making it unsuitable for applications requiring successive refinement. The goal of this work is to develop a unified framework for progressive uniform quantization of vectors, without having to sacrifice the mean-squared-error advantage of lattice quantization. A successive refinement uniform vector quantization paradigm is developed, where the codebooks in successive stages are all lattice codebooks, each in the shape of the Voronoi region of the lattice at the previous stage. The Voronoi shaped lattice codebook at each stage is called Voronoi lattice VQ (VLVQ). Measures of efficiency of successive refinement are developed. The developed methodology is applied to successively refine vectors of wavelet coefficients in the vector set-partitioning (VSPIHT) framework to obtain an embedded bitstream. The results are compared against the previous successive approximation wavelet vector quantization (SA-W-VQ) results of Sampson, da Silva, and Ghanbari ) for image coding.
Design of high capacity 3D print codes with visual cues aiming for robustness to the PS channel and external distortions

Adding high-density information to printed materials enables and improves interesting hardcopy document applications involving security, authentication, physical-electronic round tripping, item-level tagging, and consumer/product interaction. This investigation of robust and high capacity print codes aims to maximize information payload in a given printed page area, subject to robustness to channel errors including distortions introduced by the printing and scanning processes and also due to the usual degradations introduced by user manipulation of printed documents. The novel approach includes statistical print-and-scan channel characterization, designing of robust segmentation using visual cues, unsupervised Bayesian color classification with expectation-maximization algorithm for parameters estimation of a mixture of Gaussians model and design of error correction codes. Results illustrate the performance evaluated under real channel and distortions conditions. High payload is achieved with sufficient robustness to distortions resulting of regular office hardcopy document handling: print-and-scan channel and user manipulation.
JPEG-matched MRC compression of compound documents

Mixed raster content (MRC) is an ITU document compression standard (T.44) specifying both a model for multilayer representation of a compound document, and a set of allowable standardized coders for the individual layers. The model requires decomposition of a document into two image layers and a binary mask layer, but the standard does not recommend any procedure for this task. For best compression results, the decomposition method should be optimized for the layer encoders. In this paper, a high performance MRC compound document codec is presented, where the layer decomposition scheme is matched to the JPEG encoder with arithmetic coding for the foreground and background image layers. JBIG is used to code the mask layer. Integrated noise removal routines enable handling of scanned documents along with electronic ones. Resolution scalable decoding features are also implemented. The page segmenter yields a segmentation mask, which serves to separate text and other features.
Low complexity guaranteed fit compound document compression

We propose a new, very low complexity, single-pass algorithm for compression of continuous tone compound documents, known as GRAFIT (GuaRAnteed FIT), that can guarantee a minimum compression ratio of as much as 12:1 and even more, for all images in a single pass, while maintaining visually lossless quality when reproduced at resolution 300 dpi or more. The compression ratio is guaranteed in a single pass irrespective of the image being compressed. The complexity of the proposed encoder and decoder is orders of magnitude smaller than all image compression algorithms known today. For electronic compound documents, text is always compressed losslessly, and depending on the type of image, the actual compression ratio achieved may be as high as 200:1 or more. For photographic images, while the compression performance is inferior to DCT or wavelet coders, for documents at resolution 300 dpi and above, the quality is still visually lossless. Overall, performance of GRAFIT is highly competitive with the more expensive algorithms like JPEG2000, JPEG, or JPEG-LS and this performance is achieved in a single pass at much lower cost in both software and hardware.
Mediabeads: an architecture for path-enhanced media applications

Tagging digital media, such as photos and videos, with capture time and location information has previously been proposed to enhance its organization and presentation. We believe that the full path traveled during media capture, rather than just the media capture locations, provides a much richer context for understanding and "re-living" a trip experience, and offers many possibilities for novel applications. We introduce the concept of path-enhanced media, in which media is associated and stored together with a densely sampled path in time and space and we present the MediaBeads architecture for capturing, representing, browsing, editing, presenting, and searching this data. The architecture includes, among other things, novel data representations, new algorithms for automatically building movie-like presentations of trips, and novel search applications.