nils-herrmann/clean_scirepeval huggingface dataset:
An Image Enhancement Procedure for 3D Visualization of Liver CT Data

In this paper we present an image enhancement procedure for 3D visualization of CT image data in a liver study. The procedure consists of the following two steps: 1) segmentation of the liver volume from the image data; and 2) adaptive noise filtering of the liver volume for the purpose of enhancing the visibility of vascular features in the liver. The processed liver volume is visualized using the technique of maximum intensity projection (MIP), where projection images from multiple view angles are generated and visualized in cine display. Our experimental results demonstrate that the proposed procedure can greatly improve the visibility of the vascular features in the MIP images, enabling intuitive perception of the vascular system in the liver. In addition, the procedure is computationally efficient and requires little user intervention
Boosted classification of breast cancer by retrieval of cases having similar disease likelihood

In diagnostic imaging, recent studies have shown that retrieval of cases that are similar to the case being evaluated can boost its classification performance. In this work we investigate how to improve the utility of the retrieved cases by considering the similarity both in the image features and in the pathology when comparing the cases. To demonstrate the benefit of this retrieval strategy, we propose a boosted Adaboost classifier which can be adapted to the retrieved cases at a low computational cost. The proposed approach was tested on a set of 981 mammogram cases (449 malignant, 532 benign). The results show that the retrieval-boosted Adaboost classifier can significantly outperform its baseline counterpart, and that inclusion of pathology information (measured by the likelihood of malignancy) in the retrieval can further improve the classification accuracy.
Solutions of several color matching problems using convex projection theory

Uses the method of projections onto convex sets (POCS) to solve several problems related to color matching. As is well known POCS is an iterative algorithm that projects onto a sequence of constraint sets and eventually produces a solution that satisfies all prior known constraints. Among the questions addressed are: (1) what is the smallest adjustment required of a given spectrum to produce color matching in a color sensitive visual system? and (2) what contributions of basis colors will produce a match to a particular set of measurements?.<<ETX>>
Design of tapped-delay line smart antenna array using vector space projections

A tapped-delay line (TDL) structured wideband smart antenna array is designed using vector-space projection (VSP) method. The structure consists of a one-dimensional antenna array with a TDL attached to each antenna. By controlling the weight coefficients at the TDL taps using VSP method, we achieve beamforming the array over wide frequency bands. Constraints and associated projections are furnished to meet the design specifications. We demonstrate the capability of VSP method adapted to solve this array design task.
4D non-local means post-filtering for cardiac gated SPECT

Cardiac gated images in single photon emission computed tomography (SPECT) are known to suffer from increased noise due to low data counts. In this work, we investigate a post-filtering approach for SPECT images based on nonlocal means (NLM) filtering. In order to exploit the inherent correlation of the signal components among the different cardiac gates, we employ a spatiotemporal NLM filter, in which both space and temporal neighborhoods are taken into account in the similarity processing. In the experiments, we demonstrated this approach with both simulated NCAT imaging data and a set of clinical acquisition. The results show that the use of temporal smoothing in NLM could be much more effective for improving the quality of gated images than spatial smoothing.
Fast dynamic image reconstruction for gated cardiac SPECT

In this paper we propose a fast image reconstruction procedure for dynamic images from gated SPECT acquisition. We divide the cardiac cycle into a number of gate intervals as in gated SPECT, but treat the tracer distribution for each gate as a time-varying signal. To speed up the reconstruction procedure, we derive an ordered-subset version of the dynamic expectation-maximization (dEM) algorithm, and apply motion-compensated temporal filtering to enforce the correlation among the different gates. We simulated gated cardiac perfusion imaging using the gated mathematical cardiac-torso(gMCAT) phantom with Tc99m-Teboroxime dynamics. Our experimental results demonstrate that the proposed method can reconstruct gated dynamic images faster than the regular dEM algorithm, and that temporal smoothing is more effective than spatial smoothing for noise reduction.
Phase unwrapping using vector space projection methods

In this paper we explore a new approach for unwrapping of two-dimensional phase functions using vector-space projection methods. Phase unwrapping is essential for imaging systems that construct the image from phase information. Unlike some existing methods where unwrapping is performed locally on a piece-by-piece basis, this work approaches the unwrapping problem from a global point of view. The unwrapping is done iteratively, based on an extension of the Gerchberg-Papoulis algorithm, and the solution is refined over the entire region of support at each iteration. Robustness is demonstrated through its performance in a noisy environment, and its performance is compared to the least-squares algorithm developed by Pritt
Image analysis for detection of coronary artery soft plaques in MDCT images

In this paper we aim to develop a computationally- efficient image-segmentation procedure for detection and quantification of soft plaques in coronary arteries from multidetector CT images. The proposed method consists of three steps: extraction of the arterial lumen centerline, segmentation of the lumen and arterial wall separately with locally adaptive region growing, and detection of soft plaques based on effective cross- section areas of the lumen and of the wall. Preliminary results using clinical acquisitions are presented to demonstrate the effectiveness of the proposed method.
Support vector machine learning for detection of microcalcifications in mammograms

Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. In this work we propose for the first time the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The proposed method is developed and evaluated using a database of 76 mammograms containing 1120 MCs. To evaluate detection performance, free-response receiver operating characteristic (FROC) curves are used. Experimental results demonstrate that, when compared to several other existing methods, the proposed SVM framework offers the best performance.
Case-based decision strategy using outlier probability in detection of microcalcifications in mammographic lesions

In computer-aided diagnosis of clustered microcalcifications (MCs), the individual MCs in a lesion need to be first detected prior to subsequent classification as being benign or malignant. However, owing to noise characteristics and patient variability, the detection accuracy is often adversely compromised by the occurrence of false-positives (FPs) or missed MCs in detection. To deal with difficulty, we propose a case-based decision strategy in MC detection, wherein we model the potential FPs in a detector output by a stochastic neighbor graph, and the MCs are characterized as statistical outliers. In the experiments, we demonstrated this approach on a set of 146 mammograms for two MC detectors. The results show that it could not only reduce the number of FPs (by as much as 39%), but more importantly, it could also reduce case-to-case variability in detection.
Relevance vector machine learning for detection of microcalcifications in mammograms

Accurate detection of microcalcification (MC) clusters is an important problem in breast cancer diagnosis. In this paper, we propose the use of a recently developed machine learning technique - relevance vector machine (RVM) - for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier, which we developed previously. It is demonstrated that the RVM classifier achieves essentially the same detection performance as the SVM classifier, but does so with a much sparser kernel representation. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time implementation.
Locally optimum detection for additive watermarking in the DCT and DWT domains through non-Gaussian distributions

This work presents a new locally optimal blind detector for the additive transform-based image watermarking problem. Working in non-Gaussian environments, we introduce a new statistical model and its consequent application in the design of a locally optimum detection test. More specifically, we model the marginal distributions of the detail subband coefficients of DWT (Discrete Wavelet Transform) or DCT (Discrete Cosine Transform) with Student-t distribution. Since the watermark signal has low power, locally most powerful (LMP) detector is a valid choice. The experimental results show that the proposed detector has superior performance than alternative LMP detectors based on known state of the art statistical models.
Reducing the effect of false positives in classification of detected clustered microcalcifications

In classification of clustered microcalcifications (MCs), a typical approach is to extract a set of quantitative features from the MC objects for subsequent analysis by a machine learning algorithm. The presence of false positives (FPs) among the identified MCs will inevitably cause an adverse impact on the classification accuracy. In this work, we present a new feature extraction approach to accommodate the presence of potential FPs among detected MCs in a cluster. We introduce a quality factor to enhance the contribution of true MCs in the extracted features, and devise a set of cluster features to reduce the impact of FPs on the spatial distribution of the cluster. In the experiments, we demonstrated the performance of the proposed approach on a set of 186 FFDM images. The results show that it could lead to improved classification performance consistently over a wide range of detection sensitivity and FP levels.
Tomographic Reconstruction of Dynamic Cardiac Image Sequences

In this paper, we propose an approach for the reconstruction of dynamic images from a gated cardiac data acquisition. The goal is to obtain an image sequence that can show simultaneously both cardiac motion and time-varying image activities. To account for the cardiac motion, the cardiac cycle is divided into a number of gate intervals, and a time-varying image function is reconstructed for each gate. In addition, to cope with the under-determined nature of the problem, the time evolution at each pixel is modeled by a B-spline function. The dynamic images for the different gates are then jointly determined using maximum a posteriori estimation, in which a motion-compensated smoothing prior is introduced to exploit the similarity among the different gates. The proposed algorithm is evaluated using a dynamic version of the 4-D gated mathematical cardiac torso phantom simulating a gated single photon emission computed tomography perfusion acquisition with Technitium-99m labeled Teboroxime. We thoroughly evaluated the performance of the proposed algorithm using several quantitative measures, including signal-to-noise ratio analysis, bias-variance plot, and time activity curves. Our results demonstrate that the proposed joint reconstruction approach can improve significantly the accuracy of the reconstruction
Motion-compensated reconstruction of tomographic image sequences

In this paper, we study a motion-compensated approach for simultaneous reconstruction of image frames in a time sequence. We treat the frames in a sequence collectively as a single function of both space and time, and define a temporal prior to account for the temporal correlations in a sequence. This temporal prior is defined in a form of motion-compensation, aimed to follow the curved trajectories of the object motion through space-time. The image frames are then obtained through estimation using the expectation-maximization (EM) algorithm. The proposed algorithm was evaluated using the four-dimensional (4-D) gated mathematical cardiac-torso (gMCAT) D1.01 phantom to simulate gated single photon emission computed tomography (SPECT) perfusion imaging with Tc-99m-sestamibi. Our experimental results demonstrate that the use of motion compensation for reconstruction can lead to significant improvement in image quality and reconstruction accuracy.
Compensating for limited-angle effect in respiratory-gated cardiac SPECT

In respiratory-gated cardiac SPECT with amplitude binning, the acquisition time can vary greatly both between respiratory gates and among acquisition angles within each gate. If not properly accounted for, this uneven distribution in acquired data statistics will lead to limited-angle artifacts in reconstruction, which in turn can impact on the accuracy of respiratory motion correction. We investigate a compensation scheme for this uneven distribution by directly taking into account in the imaging model the actual acquisition time at different projection angles. In the experiment, we demonstrated this approach with simulated NCAT imaging data, for which quantitative results were obtained on both the reconstructed myocardium and estimated motion; we also tested the proposed approach on a set of clinical acquisition. The results show that the proposed approach could effectively suppress the limited-angle artifacts and improve the reconstruction accuracy.
A support vector machine approach for detection of microcalcifications in mammograms

Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. We propose, for the first time, the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The proposed method is developed and evaluated using a database of 76 mammograms containing 1120 MCs. To evaluate detection performance, free-response receiver operating characteristic (FROC) curves are used. Experimental results demonstrate that, when compared to several other existing methods, the proposed SVM framework offers the best performance.
New Additive Watermark Detectors Based On A Hierarchical Spatially Adaptive Image Model

In this paper, we propose a new family of watermark detectors for additive watermarks in digital images. These detectors are based on a recently proposed hierarchical, two-level image model, which was found to be beneficial for image recovery problems. The top level of this model is defined to exploit the spatially varying local statistics of the image, while the bottom level is used to characterize the image variations along two principal directions. Based on this model, we derive a class of detectors for the additive watermark detection problem, which include a generalized likelihood ratio, Bayesian, and Rao test detectors. We also propose methods to estimate the necessary parameters for these detectors. Our numerical experiments demonstrate that these new detectors can lead to superior performance to several state-of-the-art detectors.
Effect of Spatial Alignment Transformations in PCA and ICA of Functional Neuroimages

It has been previously observed that independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper, we seek to determine analytically the conditions under which this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case, we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (fMRI) data. Our analysis is applicable to both intersubject and intrasubject spatial normalization.
Learning of Perceptual Similarity From Expert Readers for Mammogram Retrieval

Content-based image retrieval relies critically on the use of a computerized measure of the similarity (i.e., relevance) of a query image to other images in a database. In this work, we explore a superivised learning approach for retrieval of mammogram images, of which the goal is to serve as a diagnostic aid for breast cancer. We propose that the most meaningful measure is one that is designed specifically to match that perceived by the radiologists in their interpretation of mammogram lesions. In our approach, we model the notion of similarity as an unknown function of the image features characterizing the lesions, and use modern machine-learning algorithms to learn this function from similarity scores collected from radiologists in reader studies. This approach is evaluated using data collected from an observer study with a set of clinical mammograms. Our results demonstrate that the proposed machine learning approach can be used to model the notion of similarity as judged by expert readers in their interpretation of mammogram images and that it can outperform alternative similarity measures derived from unsupervised learning.
Quantitative study of image features of clustered microcalcifications in for-presentation mammograms

Mammogram images are now increasingly acquired with full-field digital mammography (FFDM) systems in the clinics. Traditionally, the “for-processing” format of FFDM images is used in computer-aided diagnosis (CAD) of breast cancer. In this study, we investigate the feasibility of using “for-presentation” format of FFDM (which are more readily available) in development of CAD algorithms for microcalcification (MC) lesions. We conduct a quantitative evaluation of both the image features and the detectability of individual MCs on a set of 188 mammograms acquired in both formats. The results demonstrate that there is a high degree of agreement in the image features between the two image formats, and that a slight increase in false-positives in MC detection is observed in for-presentation images.
Reconstruction of gated cardiac SPECT using DFT basis functions

Gated cardiac single photon emission computed tomography (SPECT) is an important diagnostic tool for evaluating both myocardial perfusion and ventricular function. However, it often suffers from increased noise due to gating. In this paper we explore the use of Fourier harmonic basis functions to model the time activities across the different gates, aiming to exploit the periodicity of cardiac beating. The gated frames are reconstructed through estimation in the frequency domain along the gate dimension. We demonstrate this approach by simulating gated Tc-99m labeled sestamibi imaging based on the NURBS- based cardiac-torso (NCAT) phantom.
Optical flow estimation for a periodic images sequence

We propose a temporal modeling approach for determining image motion from a sequence of images within which the inherent motion is periodic. To exploit the periodic nature of the motion, we use a Fourier harmonic representation to model the motion field for the entire sequence. We then determine the motion field by estimating the parameters of this representation model. This joint estimation approach can take advantage of the statistics of all the available data in the image sequence. In our experiments, we applied the proposed approach to estimate the cardiac motion in gated cardiac SPECT perfusion images. Our results demonstrate that it could achieve robust estimation in the presence of strong imaging noise.
Multi-robot collaboration based on Markov decision process in Robocup3D soccer simulation game

Close collaboration and desired strategy is indispensable for humanoid robots in the RoboCup soccer competition. In order to solve the problem that the convergence rate is too low in training local strategies, this paper mainly proposed a method to optimize the parameters in decision and positioning based on reinforcement learning for soccer robots. First, Markov decision process is applied to the framework for reinforcement learning. Then, we propose a relative improved method, which is known as a Sarsa Algorithm to overcome the drawback of the low convergence rate of the average reward reinforcement learning. Meanwhile, in order to deal with the large state space problems arising in the training and improve the generalization ability, this method is applied to the Keepaway local training. The training results show that, this algorithm has a faster convergent speed than other ordinary learning algorithm.
Optical Flow Estimation for a Periodic Image Sequence

We propose a temporal modeling approach for determining image motion from a sequence of images wherein the inherent motion is periodic over time. To exploit the periodic nature of the motion, we use a Fourier harmonic representation to model the temporal evolution of the motion field for the entire sequence. We then determine the motion field simultaneously for the different image frames by estimating the parameters of this representation model, where the model order in the Fourier representation serves as a regularization parameter on the temporal coherence of the motion field. This approach can take advantage of the statistics of all the available data in the image sequence. In our experiments, we tested the proposed approach on several motion types at different noise levels, including translational motion, convergent/divergent motion, and cardiac motion. Our results demonstrate that this approach could lead to more robust estimation of the motion field in the presence of strong imaging noise compared to a frame-by-frame estimation approach.