nils-herrmann/clean_scirepeval huggingface dataset:
Feature selection for learning-machine numerical observer

It is now accepted that image quality should be evaluated using task-based criteria, such as human-observer (HO) performance in a lesion-detection task. Because an HO study is costly and time consuming, the development of a numerical observer (NO) surrogate is highly desirable. NO, like the channelized Hotelling observer (CHO), typically uses some features, i.e. numerical values, extracted from images to predict HO performance. Recently, we proposed and successfully tested a supervised-learning approach for modeling HOs with a machine-learning algorithm (namely a support vector machine). In the supervised-learning approach the goal is to identify the relationship between measured image features and HO defect likelihood scores. In this work we further explore the proposed learning approach by evaluating the image feature selection. Our preliminary results use, as a starting point, the image features as those used in CHO methodology, namely the outputs of four constant-Q frequency-band filters intended to model the human visual system, indicating that the features have significant influence on the NO accuracy in predicting HO performance.
Geometric robust watermarking based on a new mesh model correction approach

While geometric attacks are one of the most challenging problems in watermarking, random bending is probably the most difficult to handle among all geometric attacks. We present a watermarking scheme based on a new deformable mesh model to combat such attacks. The distortion is corrected using the distortion field (DF) estimated by minimizing the matching error between the meshes of the original and the attacked image. A CDMA watermarking method is used for testing the proposed method, which embeds a multi-bit signature in the DCT domain and uses mesh model correction to achieve robustness. Experiments show that the proposed scheme can survive a wide range of random bending attacks.
Parallel computation of a SPECT projection operator for a content adaptative mesh model

In this paper we explore a parallel implementation for fast calculation of a tomographic projection operator for content-adaptive mesh model (CAMM) image reconstruction. Previously we introduced 2D and 3D tomographic image reconstruction using a CAMM for single positron emission computed tomography (SPECT). The proposed parallel method is fast and allows incorporation of a non-uniform attenuation and distance-dependent spatial resolution of the imaging system. This implementation establishes a necessary step for the future development and practical use of the CAMM tomo-graphic reconstruction.
Parameter estimation in Multiple-Image Radiography

Multiple-Image Radiography (MIR) is an analyzer-based phase sensitive X-ray imaging method, which is a potential alternative for conventional radiography. MIR simultaneously generates three planar images containing information about attenuation, refraction and scattering properties of the object. The three parametric images in MIR are estimated from angular samples of the angular intensity profile (AIP) of the beam using the analyzer crystal. Like many of the modern imaging techniques, MIR is a computing imaging method and the noise in MIR, in addition to the imaging conditions, depends on the estimation of the parameters. In this work, we continue the evaluation of a new parameter estimation method for MIR by using the MIR images from a cadaveric human thumb study. We also compare this new method to the conventional estimator by using the mean square error of the estimated images and the variance of the estimators as metrics.
5D Image Reconstruction for Tomographic Image Sequences

In this paper we propose an image reconstruction procedure which aims to unify gated imaging and dynamic imaging in nuclear cardiac imaging. As in gated imaging the cardiac cycle is divided into a number of gated intervals, but the tracer distribution for each gate is treated as a time- varying signal as in dynamic imaging. By using both dynamic and motion-compensated temporal regularization, our five- dimensional (5D) reconstruction will produce an image sequence that shows three-dimensional (3D) spatial distribution of the tracer, as well as its two-dimensional (2D) temporal changes, i.e. cardiac motion and tracer's kinetics. To demonstrate the proposed reconstruction method, we simulated gated cardiac single photon emission computed tomography (SPECT) imaging using the NURBS-based cardiac-torso (NCAT) phantom with Tc99m-Teboroxime as the imaging agent. The results show that our method can reconstruct a meaningful 5D data array and motion- compensated temporal regularization can lead to improved reconstruction than using spatial smoothness alone.
Detection of cardiac abnormalities from MRI sequences by using a deformable mesh model

In this work, we explore the potential of estimated ventricle motion, using a deformable mesh model (DMM), to detect cardiac pathologies such as hypertension and mitral regurgitation in MRI cardiac gated image sequences. In DMM, left ventricle motion was estimated by deforming a 3D-mesh along pixel-intensity variations of combined tagged and cine MRI sequences. Next, dense motion fields obtained from DMM were used to compute 3D torsion maps for the LV using a B-Spline model. Features extracted from the torsion maps were used for detection of hypertension and mitral regurgitation by performing Fisher Discriminant Analysis. Finally, detection performance of DMM motion was compared to other known motion-tracking approaches, such as Feature Based (FB) analysis and Unwrapped Phase Strain (SUP).
Multi-modality tomographic image reconstruction using mesh modeling

In the proposed approach a mesh model uses information obtained from an anatomical MR image to aid in reconstruction of PET images. The aim is to improve spatial resolution and quantitative accuracy of the PET image by using anatomical boundary information from the MR image. The mesh approach accomplishes this by using spatially adaptive spatial sampling and smoothing in the PET reconstruction. Our preliminary results demonstrate that this mesh-based approach to multi-modality PET reconstruction can achieve good results at low computational cost.
Sampling strategies in multiple-image radiography

Multiple-Image Radiography (MIR) is an analyzer-based phase-sensitive x-ray imaging method, which is a potential alternative to conventional radiography. MIR simultaneously generates three planar images containing information about scattering, refraction and absorption properties of the object. These parametric images are acquired by sampling the angular intensity profile of the beam passing through the object at different positions of the analyzer crystal. Like many of the modern imaging techniques, MIR is a computing imaging method and the noise in MIR, in addition to the imaging conditions, depends also on the estimation of the parameters. In this work, we use Cramer-Rao lower bound to quantify the noise in MIR estimated images and investigate the effect of different sampling strategies at the analyzer on this bound. We also evaluate the performance of an estimator with respect to this bound.
Learning a Channelized Observer for Image Quality Assessment

It is now widely accepted that image quality should be evaluated using task-based criteria, such as human-observer performance in a lesion-detection task. The channelized Hotelling observer (CHO) has been widely used as a surrogate for human observers in evaluating lesion detectability. In this paper, we propose that the problem of developing a numerical observer can be viewed as a system-identification or supervised-learning problem, in which the goal is to identify the unknown system of the human observer. Following this approach, we explore the possibility of replacing the Hotelling detector within the CHO with an algorithm that learns the relationship between measured channel features and human observer scores. Specifically, we develop a channelized support vector machine (CSVM) which we compare to the CHO in terms of its ability to predict human-observer performance. In the examples studied, we find that the CSVM is better able to generalize to unseen images than the CHO, and therefore may represent a useful improvement on the CHO methodology, while retaining its essential features.
Machine Learning in Medical Imaging

This article will discuss very different ways of using machine learning that may be less familiar, and we will demonstrate through examples the role of these concepts in medical imaging. Although the term machine learning is relatively recent, the ideas of machine learning have been applied to medical imaging for decades, perhaps most notably in the areas of computer-aided diagnosis (CAD) and functional brain mapping. We will not attempt in this brief article to survey the rich literature of this field. Instead our goals will be 1) to acquaint the reader with some modern techniques that are now staples of the machine-learning field and 2) to illustrate how these techniques can be employed in various ways in medical imaging.
Generalization evaluation of numerical observers for image quality assessment

It is now widely accepted that image quality should be evaluated by using task-based criteria, such as human-observer (HO) performance in a lesion-detection task. Because a HO study is typically costly and time consuming, development of a numerical observer (NO) surrogate is highly desirable. Recently we proposed a supervised-learning approach for modeling HO with a numerical observer, where a machine-learning algorithm is used to model the relationship between measured image features and HO scores. In this work, we further develop and evaluate this approach. We develop a NO in a lesion-detection task using a channelized support vector machine (CSVM), and compare its performance against the widely used channelized Hotelling observer (CHO). In particular we employ a cross-validation procedure to quantify the generalization ability of a NO. It allows us to evaluate how well a NO can perform when it is trained on one set of images, but tested on a different set of images. Our evaluation results demonstrate that while both CHO and CSVM can generalize well when training and test images are both reconstructed in the same way, the CSVM can generalize better when the test images are reconstructed in a different way from that of training images.
Similarity based clustering using the expectation maximization algorithm

In this paper we present a new approach for clustering data. The clustering metric used is the normalized cross-correlation, also known as similarity, instead of the traditionally used Euclidean distance. The main advantage of this metric is that it depends on the signal shape rather than its amplitude. Under an assumption of an exponential probability model that has several desirable properties, the expectation-maximization (EM) framework is used to derive two iterative clustering algorithms. Numerical experiments are presented using simulated data in a dynamic positron emission topography study of the brain. Initial results demonstrate that the proposed method achieves better performance than several existing clustering methods.
Spatially-adaptive temporal smoothing for reconstruction of dynamic and gated image sequences

In this paper we propose a method for spatio-temporal reconstruction of dynamic or gated image sequences. In a method we proposed previously, temporal smoothing in a Karhunen-Loeve (KL) transform domain was used prior to reconstruction to reduce the effect of noise. Unlike the Bayesian priors that are usually used in image reconstruction, temporal KL smoothing is a data-driven approach that takes advantage of the fact that the desired part of the data is characterized by strong inter-frame correlations, whereas the noise is uncorrelated. In this paper we improve on one of our group's previous techniques by making the temporal smoothing adapt spatially to local characteristics in the projection data. This improves the noise performance of the temporal smoothing, while significantly lessening the possibility of signal distortion. In the proposed method, spatial regions of the projection data sequence having similar time characteristics are identified by an unsupervised k-means clustering algorithm. A different KL transformation is designed for each spatial region in projection space, adapting the smoothing to the local temporal behavior. Finally, images are reconstructed from the smoothed projections by using the expectation-maximization (EM) algorithm. Experimental computer simulation results are shown that demonstrate potential improvements in image quality obtained by this technique for dynamic and gated imaging applications in brain, lesion, and cardiac imaging.
Comparison between different methods for parametric image estimation in analyzer-based phase contrast images

Analyzer-based phase contrast imaging (ABI) is an emerging imaging technique capable of visualizing complex interactions between X-rays and an object. Like many of the modern imaging techniques, ABI is a computed imaging method where parametric images are estimated from raw data. ABI raw data is acquired by measuring (sampling) the angular intensity profile (AIP) of the X-ray beam passed through the object at different positions of the analyzer crystal. ABI can simultaneously generate a number of planar parametric images containing information about absorption, refraction, and scattering properties of the object. In this manuscript, we compare several parametric image estimation methods with a newly proposed method based on the maximum likelihood estimation. In addition, we evaluate the impact of angular sampling positions on parametric image estimation accuracy as measured by bias and variance.
Estimating Tl-201 redistribution in the heart at rest using stress perfusion SPECT imaging

From our measurements in a clinic setting of total counts in the heart during Tl-201 stress and rest imaging, we found the difference between the two imaging time points to be approximately 30%-40%, making the washout rate much slower than Tc-99m Teboroxime. The goal of this study was to investigate ways to accurately estimate the dynamic behavior of Tl-201 directly after exercise stress in an effort to predict the activity distribution at rest.
Numerical Surrogates for Human Observers in Myocardial Motion Evaluation From SPECT Images

In medical imaging, the gold standard for image-quality assessment is a task-based approach in which one evaluates human observer performance for a given diagnostic task (e.g., detection of a myocardial perfusion or motion defect). To facilitate practical task-based image-quality assessment, model observers are needed as approximate surrogates for human observers. In cardiac-gated SPECT imaging, diagnosis relies on evaluation of the myocardial motion as well as perfusion. Model observers for the perfusion-defect detection task have been studied previously, but little effort has been devoted toward development of a model observer for cardiac-motion defect detection. In this work, we describe two model observers for predicting human observer performance in detection of cardiac-motion defects. Both proposed methods rely on motion features extracted using previously reported deformable mesh model for myocardium motion estimation. The first method is based on a Hotelling linear discriminant that is similar in concept to that used commonly for perfusion-defect detection. In the second method, based on relevance vector machines (RVM) for regression, we compute average human observer performance by first directly predicting individual human observer scores, and then using multi reader receiver operating characteristic analysis. Our results suggest that the proposed RVM model observer can predict human observer performance accurately, while the new Hotelling motion-defect detector is somewhat less effective.
Content-adaptive 3D mesh modeling for representation of volumetric images

In this work we present a fast, content-adaptive approach for three-dimensional (3D) mesh representation of volumetric images. A rigorous error bound is derived for a 3D mesh representation of a volumetric image based on the theory of function interpolation. From this result, a computationally efficient algorithm is proposed for adaptive placement of mesh nodes (hence mesh elements) in the 3D image domain according to the image content. Experimental results demonstrate that a highly compact and accurate representation of volumetric images can be achieved at low computational cost by the proposed algorithm.
Optimization of the internal noise models for channelized Hotelling observer

The channelized Hotelling observer (CHO) has become a widely used approach for evaluating medical image quality, acting as a surrogate for human observers in early-stage research on assessment and optimization of imaging devices and algorithms. Its popularity stems from experiments showing that, when an internal-noise model is introduced, the CHO's detection performance can be tuned to correlate well with human observers' detection performance. Typically, this tuning is achieved using example data obtained from human observers. Thus, it can be argued that this tuning step is essentially a model training exercise; therefore, just as in supervised learning, it is essential to test the CHO model on a set of data that is distinct from that used to tune the model. Furthermore, the test data should be significantly different from the training data if the CHO is to provide useful insight about new imaging algorithms or devices. Motivated by these considerations, in this work a train-test approach was proposed, with new models selection criterions, and used to evaluate ten established internal noise models utilizing four different channel models. I also propose a new internal noise model (although not the main intention of this work) which I, incidentally, find outperforms the ten established models. The results show that the proposed train-test approach is necessary, with the newly proposed model selection criterions, for avoiding spurious conclusions. It was also demonstrate that, in some CHO models, the optimal internal-noise parameter is very sensitive to the choice of training data; therefore, these models are prone to overfitting, and will not likely generalize well to new data.
Simulation of a table-top analyzer based phase contrast imaging system

Analyzer-based imaging (ABI) belongs to a broader family of phase-contrast (PC) X-ray imaging modalities. Unlike the conventional X-ray radiography, which measures only X-ray absorption, in PC imaging one can also measure the X-rays refraction and scatter properties of the imaged object. It has been shown that refraction imaging provide a better contrast when imaging the soft tissue than the traditional absorption-contrast modalities. In this paper, we introduce a simulation tool specifically designed to simulate the ABI systems with a conventional polychromatic X-ray source. By utilizing the ray tracing and physical principles of ideal crystals diffraction theory our simulation tool can predict the ABI X-ray beam: spatial profile, energy and angular spectrum and total throughput (photon count) at the detector. In addition, we can incorporate objects simulations that allow us to assess the image quality achieved for the different imaging system setups. Using this approach, we demonstrate a novel method to image using two K-alpha emission lines of a tungsten X-ray source.
Tomographic image reconstruction for systems with partially-known blur

In tomographic image reconstruction, it is usually assumed that the system matrix is known exactly, although this is not usually the case in practice. We investigate the potential benefit of modeling the system matrix as the sum of a known part and an unknown random error. Using some of simplifying assumptions, we develop a penalized weighted least squares (PWLS) reconstruction algorithm for this problem. Our experiments indicate that this approach can, indeed lead to significant improvements in the reconstructed image, both visually and quantitatively.
A fast algorithm for accurate content-adaptive mesh generation

Previously, we proposed a computationally efficient approach to content-adaptive mesh generation used for image representation (see Lee, J. et al.,  2000). We now provide a theoretical basis for that method, which leads to an improved version of the algorithm. An error bound is derived for a mesh representation of an image based on the theory of function interpolation. From this result, a more accurate scheme is proposed for placement of mesh elements in the image domain according to the image content. Experimental results, compared to other methods, show that a highly accurate image representation can be obtained at extremely low computational cost by the proposed technique.
Digital watermarking robust to geometric distortions

In this paper, we present two watermarking approaches that are robust to geometric distortions. The first approach is based on image normalization, in which both watermark embedding and extraction are carried out with respect to an image normalized to meet a set of predefined moment criteria. We propose a new normalization procedure, which is invariant to affine transform attacks. The resulting watermarking scheme is suitable for public watermarking applications, where the original image is not available for watermark extraction. The second approach is based on a watermark resynchronization scheme aimed to alleviate the effects of random bending attacks. In this scheme, a deformable mesh is used to correct the distortion caused by the attack. The watermark is then extracted from the corrected image. In contrast to the first scheme, the latter is suitable for private watermarking applications, where the original image is necessary for watermark detection. In both schemes, we employ a direct-sequence code division multiple access approach to embed a multibit watermark in the discrete cosine transform domain of the image. Numerical experiments demonstrate that the proposed watermarking schemes are robust to a wide range of geometric attacks.
Active learning for image quality assessment by model observer

In medical imaging, it is widely accepted that image quality should be evaluated using a task-based approach in which one evaluates human observer performance for a given diagnostic task. Unfortunately, human observer studies with expert readers are costly and time-demanding. To confront this problem, model observers (MO) have been used as surrogates for human observers. MOs typically can accurately predict human diagnostic performance but some types of MOs require sets of images and human observer scores for tuning (training). Current literature does not provide guidance on how to choose the training data set. Therefore, in this work we present a heuristic active learning approach, using uncertainty sampling, to the problem of selecting good MOs training data sets. The presented results indicate that the proposed data set selection approach, together with a learning model observer based on the relevance vector machine, has excellent performance in predicting human observer performance as measured by the area under the receiver operating curve (AUC).