nils-herrmann/clean_scirepeval huggingface dataset:
Multichannel regularized recovery of compressed video sequences

In this paper, we propose a multichannel regularized recovery approach to ameliorate coding artifacts in compressed video. The major advantage of the proposed approach is that both temporal and spatial correlations in a video sequence can be exploited to complement the compressed video data. In particular a temporal regularization term is introduced to enforce smoothness along the motion trajectories defined by the transmitted motion vectors for motion compensation. Several forms of temporal regularization with different computational complexity are considered. Based on the proposed approach, recovered images are obtained from the compressed data using the well-known gradient-projection algorithm. Moreover, an iterative algorithm is proposed for the determination of regularization parameters at the coder side. A number of numerical experiments using several H.261 and H.263 compressed streams are presented to evaluate the performance of the proposed recovery algorithms. Results from these experiments demonstrate that the use of temporal regularization ran yield significant improvement in the quality of the recovered images-in terms of both visual evaluation and objective peak-signal-to-noise (PSNR) measure.
DWT-based additive image watermarking using the Student-t prior

In this work, a class of new blind watermark detectors is proposed for the DWT (Discrete Wavelet Transform)-based additive image watermarking problem. More specific, we model the marginal subband wavelet distributions with the Student-t probability density function (pdf) deriving a new watermark detector. The proposed detector shows high performance with regard to the watermark detection and increased robust properties against intentional or unintentional attacks. Experimental results on real images demonstrate these properties comparing the proposed detector with other state of the art methods in the transform domain.
RETRIEVAL-DRIVEN MICROCALCIFICATION CLASSIFICATION FOR BREAST CANCER DIAGNOSIS

In this paper, a content-based mammogram retrieval system is developed and evaluated to improve the performance of both human and numerical observers in breast cancer diagnosis. We previously developed a machine learning approach for modeling the similarity measure between two lesion mammograms from expert observer studies for mammogram retrieval. In this work we investigate how to use the retrieved similar cases as references to improve a numerical classifier's performance. The rationale is that by adaptively incorporating proximity information to the cost function of a classifier, it can help to improve the classification accuracy, thereby leading to an improved "second opinion" to radiologists. Our experiment results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve
Exploiting rotation invariance with SVM classifier for microcalcification detection

In previous work we developed a support vector machine (SVM) approach for detection of microcalcifications (MCs) in mammogram images, which was demonstrated to outperform several existing methods for MC detection in the literature. In this work, we explore whether we can further improve the performance of the SVM detector by exploiting the fact that MCs are inherently invariant to their spatial orientation in a mammogram image. We consider two different techniques for incorporating invariance into SVM, of which one is virtual support vector SVM (VSVM) and the other is tangent vector SVM (TV-SVM). In the experiments these techniques were tested on a database of 200 mammograms containing a total of 5,211 MCs. The results show that both techniques can improve the performance in discriminating MCs from the image background, and TV-SVM achieved the best performance.
A support vector machine approach for detection of microcalcifications

We investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to outperform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.
Relevance feedback based on incremental learning for mammogram retrieval

In this work we explore a new technique for relevance feedback in a learning-based framework for retrieval of relevant mammogram images from a database, for purposes of aiding diagnoses. Our goal is to adapt online the learning procedure in accordance with user responses without the need to repeat the training procedure. Toward this end we develop a relevance feedback approach based on the concept of incremental learning developed recently in the theory of support vector machines. The proposed approach is demonstrated using clustered microcalcifications extracted from a database consisting of 76 mammograms.
4D reconstruction of gated cardiac SPECT using fourier harmonics

We propose a four-dimensional reconstruction method for noise reduction in gated cardiac SPECT images. Aiming to exploit the periodic nature of cardiac motion, we use a Fourier harmonic representation to model the image activities along the gated temporal dimension. We develop a penalized least-squares approach to reconstruct the different harmonics in the representation model, which also takes into account spatial smoothing. The proposed method is demonstrated using simulated cardiac perfusion imaging based on the NURBS-based cardiac-torso (NCAT) phantom. Our results show that it could lead to improved reconstruction of the gated images.
Gated cardiac SPECT using different motion models

Gated cardiac SPECT imaging suffers from several degrading factors ranging from limited data counts to motion blur. In this work we investigate the use of different optical flow estimation methods for motion-compensated temporal processing in gated SPECT, of which the goal is to improve the accuracy of image reconstruction. In particular, we examine how much room is still left in optical flow estimation toward improving the reconstructed image quality. In our experiments we conducted a quantitative evaluation using simulated imaging study with multiple noise realizations from the NCAT phantom. As an upper bound, the known motion in NCAT was used for reconstruction. The results show that 4D reconstruction with a periodic optical flow model can achieve results almost matching that from the known motion.
Utility of 4D reconstruction for low-dose cardiac gated SPECT

By modeling major degrading factors in SPECT imaging along with motion-compensated temporal denoising, 4D reconstruction is demonstrated to significantly improve the quality of reconstructed images in cardiac gated SPECT (GSPECT) at the standard dose level. The utility of 4D reconstruction for radiation dose reduction in GSPECT has not been studied despite increased concern in population based risk due to its ionizing radiation. In this work, we investigate the potential of 4D reconstruction on reducing the imaging dose using Monte Carlo simulation of GSPECT imaging of a realistic human phantom at different dose levels. We use a number of metrics to quantify the quality of reconstructed images. The results demonstrate that 4D reconstruction can achieve better or comparable reconstruction at half dose compared to conventional clinical reconstruction at standard dose.
Cardiac perfusion defect detection using gated dynamic SPECT imaging

In our previous work we proposed a dynamic image reconstruction procedure for gated cardiac imaging, of which the goal is to obtain a single image sequence that shows simultaneously both cardiac motion and tracer distribution change over time. In this work, we further develop and demonstrate the feasibility of this procedure for perfusion defect detection in gated cardiac imaging. We conduct Fisher's linear discriminant analysis on the reconstructed dynamic images, and derive quantitative measures to differentiate defects from normal myocardial perfusion. Results are presented to demonstrate the proposed development using simulated gated cardiac imaging with the NURBS-based cardiac-torso (NCAT) phantom.
Feature saliency analysis for perceptual similarity of clustered microcalcifications

Retrieving and presenting a set of known lesions similar to the one being evaluated has the potential to improve the performance of radiologists in diagnosis of breast cancer with clustered microcalcifications (MCs). In this work, we investigate how perceptually similar cases are related to each other in terms of their image features. We apply supervised learning and feature saliency analysis to determine the most relevant image features based on similarity ratings collected from a group of radiologists on 1,000 image pairs. The results demonstrate that the relevant features are consistent in radiologists' similarity ratings among different lesions, which include geometric clustering features (size and shape) and MC features (size, contrast and shape).
Effects of motion estimation methods on 4D gated cardiac SPECT reconstruction

Cardiac gated SPECT imaging suffers from several degrading factors including limited data counts, depth-dependent blur, attenuation, scatter, and motion blur. In recent work we demonstrated that use of temporal processing could lead to the most improvement in 4D reconstruction. In this work we investigate how motion estimation models can affect the reconstruction in 4D, and in particular, we examine how much room is still left in optical flow estimation toward improving the image quality of reconstruction. In our experiments we conducted a thorough, quantitative evaluation using simulated imaging study with multiple noise realizations from the NCAT phantom. As an upper bound, the known motion from NCAT was used for reconstruction. The results show that 4D reconstruction with a periodic optical flow model can achieve results almost matching that with the known motion.
Direct parametric reconstruction of gated dynamic cardiac spect

In our recent work, we demonstrated the feasibility of an image reconstruction procedure for unifying gated and dynamic imaging, which could yield valid kinetic information for discriminating between normal and perfusion defects. Motivated by this success, in this work we investigate a direct reconstruction approach to determine a sequence of gated, kinetic parameter images from a single acquisition, of which the goal is to provide information simultaneously for both tracer kinetics and wall motion. To combat the greatly under-determined nature of the problem, we apply a smoothness constraint to exploit the similarity among the different gates. The parameter images of the different gates are then determined jointly using maximum a posteriori (MAP) estimation from all the available image data.
Spatial distribution modeling for detection of clustered microcalcifications

We propose a spatial point-process modeling approach to improve the detection of clustered microcalcifications (MCs) in mammogram images. Apart from the predominant approach for MC detection, in which individual MCs in an image are first detected independently and then grouped into clusters, our proposed approach aims to incorporate the spatial clustering property of the MCs directly into the detection process (i.e., MCs tend to appear in small clusters). We model the MCs by a marked point process (MPP) in which spatially neighboring MCs are interactive with each other. The detection is achieved through maximum a posteriori (MAP) estimation of the parameters of the MPP model. The proposed approach was evaluated with a dataset of 141 clinical mammograms, and the results show that it could yield improved performance compared with a recently proposed SVM detector.
Image retrieval based on similarity learning

We explore the use of various learning algorithms to predict the user's measure of similarity between a given query image and images in a database. Our aim is to obtain a similarity coefficient, for use in image retrieval, that more accurately reflects that of the user. The performance of a variety of learning machines was evaluated using statistical resampling to estimate the prediction error and retrieval effectiveness. The proposed approach was demonstrated using synthetic shape and texture examples. The results of the study are very promising, especially those obtained by the general regression neural network and the support vector machine/radial basis function method.
4-D Reconstruction With Respiratory Correction for Gated Myocardial Perfusion SPECT

Cardiac single photon emission computed tomography (SPECT) images are known to suffer from both cardiac and respiratory motion blur. In this paper, we investigate a 4-D reconstruction approach to suppress the effect of respiratory motion in gated cardiac SPECT imaging. In this approach, the sequence of cardiac gated images is reconstructed with respect to a reference respiratory amplitude bin in the respiratory cycle. To combat the challenge of inherent high-imaging noise, we utilize the data counts acquired during the entire respiratory cycle by making use of a motion-compensated scheme, in which both cardiac motion and respiratory motion are taken into account. In the experiments, we first use Monte Carlo simulated imaging data, wherein the ground truth is known for quantitative comparison. We then demonstrate the proposed approach on eight sets of clinical acquisitions, in which the subjects exhibit different degrees of respiratory motion blur. The quantitative evaluation results show that the 4-D reconstruction with respiratory correction could effectively reduce the effect of motion blur and lead to a more accurate reconstruction of the myocardium. The mean-squared error of the myocardium is reduced by 22%, and the left ventricle (LV) resolution is improved by 21%. Such improvement is also demonstrated with the clinical acquisitions, where the motion blur is markedly improved in the reconstructed LV wall and blood pool. The proposed approach is also noted to be effective on correcting the spill-over effect in the myocardium from nearby bowel or liver activities.
Adaboost with dummy-variable modeling for reduction of false positives in detection of clustered microcalcifications

Linear structures are a major contributor to false-positives (FPs) in detection of clustered microcalcifications (MCs) in mammograms. We propose a unified classifier approach to incorporate the dichotomous effect of linear structures in MC detection, the purpose being to suppress the FPs associated with linear structures. We introduce a dummy variable in the classifier model as in traditional regression analysis, the role of which is to adapt the input features to the classifier according to the presence of linear structures. In the experiment we demonstrate the proposed approach by using Adaboost decision stumps as the unified classifier. The results on a set of 200 mammogram images (all containing clustered MCs) show that it could reduce the FPs in an existing SVM detector by up to 47.3% with the true-positive rate at 85%.
An improved periodic optical flow model for cardiac gated image reconstruction

Recently we developed a periodic modeling approach for determining the optical flow in a periodic image sequence, which is demonstrated to be beneficial for noise reduction in motion-compensated 4D reconstruction of cardiac gated images. In this approach, a Fourier harmonic model is used to exploit the temporal continuity and periodicity of the motion field in the sequence. In this work, we further develop this approach by exploring the use of a piecewise spatial smoothness constraint (in the form of total variation) on the motion field, which can better accommodate the discontinuity of the motion field at an object boundary. In the experiments, we demonstrated this approach for 4D reconstruction of gated cardiac SPECT images. Our results show that it could lead to further improved reconstruction of the myocardium in spite of strong imaging noise.
Estimating the level of false positives in detection of microcalcifications in mammogram images

In computerized detection of clustered microcalcifications (MCs) from mammogram images the occurrence of false positives (FPs) varies greatly from case to case. In this work, we develop a probabilistic modeling approach to estimate the number of individual FPs present in a detected MC lesion. We describe the number of true positives (TPs) by a Poisson-Binomial probability distribution, wherein a logistic regression model is trained to determine the probability for a detected MC to be a TP based on its characteristics in the detector output. We demonstrate the proposed approach on a set of 188 full-field digital mammography images with two existing MC detectors [difference-of-Gaussians (DoG) and SVM detector]. The results show that the error level in the estimated number of FPs can be as low as 2.51 on average when the number of FPs is as high as 11.38 in a detected MC lesion.
ADAPTIVE REGULARIZATION USING B-SPLINES FOR GATED DYNAMIC CARDIAC SPECT

Recently we proposed a joint reconstruction approach for dynamic cardiac images from a gated acquisition in single photon emission computed tomography (SPECT), where the time evolution at each image pixel is regulated by a B-spline function. Such an approach was demonstrated to be very effective for coping with the greatly underdetermined nature of the problem. In this work we further develop this approach by investigating the use of an adaptive smoothing prior on the spline control points based on their contribution to the acquired data counts. The proposed approach is evaluated using a dynamic version of the 4D gated mathematical cardiac-torso (gMCAT) phantom to simulate a gated SPECT perfusion acquisition with Tc99m Teboroxime. Our results demonstrate that the use of the newly introduced prior can improve the accuracy of the reconstruction
Image retrieval for computer-aided diagnosis of breast cancer

In this work we propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CAD). The traditional approach in CAD is to first train a pattern-classifier based on a set of existing training samples, and then apply this classifier to subsequent new cases. In our proposed approach, we will first apply image-retrieval to obtain a set of lesion images from a library of known cases that have similar image features to a case being diagnosed (i.e., query). These retrieved cases are then used to optimize a pattern-classifier toward boosting its classification accuracy on the query case. In our experiments the proposed retrieval-driven approach was tested on a library of mammogram images from 589 cases (331 benign, 258 malignant), and was demonstrated to yield significant improvement in classification performance.
Joint motion correction and image reconstruction in respiratory-gated SPECT

Due to the irregularity in respiratory patterns observed clinically, the acquired data in cardiac SPECT with respiratory-gating can exhibit high variability among both gate intervals and acquisition angles. Such variability can lead to differing noise characteristics among respiratory gates, which would adversely affect the accuracy of motion estimation. To address this difficulty, we develop a joint motion-estimation and image-reconstruction approach, in which the respiratory motion is estimated simultaneously along with the source distribution. In the experiments, we demonstrated this joint estimation-reconstruction approach with both quantitative simulated NCAT data and a set of clinical acquisition. We also explored its robustness with reduced imaging dose. The results show that the proposed approach can further improve the reconstructed myocardium over a pre-reconstruction motion-estimation approach.
Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

Breast cancer is the second-most common and leading cause of cancer death among women. It has become a major health issue in the world over the past 50 years, and its incidence has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The purpose of this paper is to provide an overview of recent advances in the development of CAD systems and related techniques. We begin with a brief introduction to some basic concepts related to breast cancer detection and diagnosis. We then focus on key CAD techniques developed recently for breast cancer, including detection of calcifications, detection of masses, detection of architectural distortion, detection of bilateral asymmetry, image enhancement, and image retrieval.
Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields

Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotheraphy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.