nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the effect of incremental dose reduction on perfusion defect detection employing hybrid cardiac perfusion SPECT slices

Accruing enough patient studies with an acceptable gold standard diagnosis to evaluate reconstruction strategies is costly and cumbersome. We recently improved and streamlined a method developed earlier for inserting cardiac perfusion defects into SPECT slices of hearts deemed normal by experienced physicians. In this study we employed our improved methodology to study the effect of incremental dose reduction on perfusion defect detection. All patients included in this study were under IRB approval with written consent. Imaging consisted of list-mode acquisitions during stress Tc-99m sestamibi perfusion SPECT on a BrightView SPECT/CT (Philips, Cleveland, OH) employing the standard clinical protocol. Patient body and respiratory motion tracking were performed to correct SPECT slices during reconstruction. A method was developed to randomly select fractions of list-mode events for inclusion in decreased dose acquisitions spanning the total acquisition time. The original data were then reconstructed with a combination of respiratory and rigid-body motion compensation and perfusion defects inserted to reflect a 50% reduction in wall counts. The same defects were subsequently inserted into the lower dose acquisitions. Finally, the hybrid projections were reconstructed with and without respiratory motion compensation and evaluated using polar map quantitation comparing the percentage reduction of the counts in the lower dose defects with that in the original hybrid acquisitions. Also, success was assumed when the polar map software flagged the inserted defect as a region with disease according to the ASNC guidelines. In our preliminary evaluation, we were able to reduce the dose to 25% of the original counts and obtain similar decreases in defect counts while still been flagged as diseased. However, other distractors (noise, respiratory motion when not corrected) now clearly influence the accuracy in location and size of the defects. Therefore, other processing methods not yet clinically available should be explored, especially for dose reduction below 50% of that used in standard acquisition protocols.
Dynamic Image Reconstruction using Temporally Adaptive Regularization for Emission Tomography

Temporal basis functions have been found to be effective for regularizing the time-varying image activities in dynamic emission tomography. By modelling the tracer distribution function at individual pixels as a linear combination of a set of basis functions, the reconstruction problem becomes that of estimating the weights of the basis functions. In this work, we explore the use of temporally adaptive regularization in the basis function domain, where spatial smoothing is enforced in an adaptive fashion according to the time-varying data statistics. In our experiments the proposed method was demonstrated using simulated Tc99m-Teboroxime SPECT imaging with the gated mathematical cardiac-torso (gMCAT) phantom. Our results show that the proposed approach can lead to more accurate reconstruction of the time activities, which is important for differentiation between a perfusion defect and the normal myocardium.
Microcalcification Classification Assisted by Content-Based Image Retrieval for Breast Cancer Diagnosis

In this paper we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms is modeled after expert observers. In this work we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adap-tively incorporating local proximity information into a classifier, it can help improve its classification accuracy, thereby leading to an improved "second opinion" to radiologists. Our experimental 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.
Spatial density modeling for discirminating between benign and malignant microcalcification lesions

Accurate diagnosis of microcalcification (MC) lesions in mammograms is an important but challenging clinical task in early cancer detection. In this work, we investigate how to extract salient and robust quantitative features for discriminating between benign and malignant cases in the presence of inaccuracy in MC detection. We propose to use a spatial density function (SDF) to characterize the spatial distribution of the MCs in a cluster, aimed to better accommodate the potential inaccuracy in the detected MCs. We demonstrate this approach on a set of commonly used features for clustered MCs. The proposed approach was tested on a set of 640 cases. The results show that the SDF features are robust to variations in MC detection while achieving better class separation.
High dynamic range imaging using vector space projection

The purpose of HDR imaging is to increase the dynamic range of the image intensity of a scene which otherwise would be limited by a normal imaging technique/device. A commonly used approach to HDR is to acquire images with multiple exposures of the same scene. In this paper, an iterative algorithm is explored for the first time to recover an HDR image from multiple exposures based on the method of projections onto constraint sets. To construct appropriate constraint sets, noise sources in pixels and sensor structures are discussed. An equivalent noise characteristics at the sensor output is modeled as the Gaussian Random Process. Base on noise characteristics and sensor response features, a group of pixel-wise constraint sets are defined to measure the estimation errors. Utilizing the knowledge on neighbourhood pixels, the local spatial constraint sets on the image are also defined. The method of vector-space projections is applied onto these constraint sets to reconstruct the HDR image using multiple exposures of the same scene.
Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class

Prostate cancer is one of the leading causes of death from cancer among men in the United States. Currently, high-resolution magnetic resonance imaging (MRI) has been shown to have higher accuracy than trans-rectal ultrasound (TRUS) when used to ascertain the presence of prostate cancer. As MRI can provide both morphological and functional images for a tissue of interest, some researchers are exploring the uses of multispectral MRI to guide prostate biopsies and radiation therapy. However, success with prostate cancer localization based on current imaging methods has been limited due to overlap in feature space of benign and malignant tissues using any one MRI method and the interobserver variability. In this paper, we present a new unsupervised segmentation method for prostate cancer detection, using fuzzy Markov random fields (fuzzy MRFs) for the segmentation of multispectral MR prostate images. Typically, both hard and fuzzy MRF models have two groups of parameters to be estimated: the MRF parameters and class parameters for each pixel in the image. To date, these two parameters have been treated separately, and estimated in an alternating fashion. In this paper, we develop a new method to estimate the parameters defining the Markovian distribution of the measured data, while performing the data clustering simultaneously. We perform computer simulations on synthetic test images and multispectral MR prostate datasets to demonstrate the efficacy and efficiency of the proposed method and also provide a comparison with some of the commonly used methods.
Ordered spatial subsets for faster reconstruction in spect

Iterative reconstruction is now becoming increasingly used in clinical SPECT reconstruction. Ordered subsets (OS) algorithms have been particularly attractive for accelerating the convergence in iterative reconstruction. Traditionally, the ordered subsets in an OS algorithm are formed by grouping the sinogram data according to the angular positions of the projection. In this work, we propose an alternative approach to form the ordered subsets by sub-sampling the spatial locations in the detector array so as to exploit the depth-dependent blur in the detector response. We demonstrate the proposed approach in the context of EM reconstruction and the results in our experiment show that it can indeed accelerate the convergence of EM; more importantly, it can further improve the convergence rate of traditional OS reconstruction when used together with angular ordered subsets.
Point process modeling for determining detection accuracy of mammographic microcalcifications

The occurrence of false positives (FPs) varies greatly from case to case in computerized detection of clustered microcalcifications (MCs) from mammogram images. In this work, we explore using a stochastic modeling approach to estimate the number of individual FPs present in a detected MC lesion. We model the spatial occurrence of FPs in the MC detector output by a Poisson spatial point process, of which the parameters are estimated from the detected objects both in the lesion region and in a reference region in the immediate neighborhood of the lesion. We demonstrate the proposed approach on a set of 188 full-field digital mammography images with two existing MC detectors. The results show that on average the error level in the estimated number of FPs is 3.62 when the actual number of FPs is at the level of 11.38 in a detected MC lesion.
A new approach for image-content adaptive mesh generation

A new, effective approach is proposed for content-adaptive mesh generation used for image representation. The proposed approach employs the Floyd-Steinberg (1975) method, a classical error-diffusion algorithm for image halftoning, to distribute the mesh nodes in the domain of an image based on its gradient magnitude. Consequently, fine mesh elements are placed automatically in regions of the image containing high-frequency features while coarse mesh elements are used in smooth regions. The proposed algorithm is non-iterative and easy to implement. Numerical results demonstrate that it can yield a compact, accurate representation of images.
Reduction of false positive detection in clustered microcalcifications

Linear structures are a major source of false positives (FPs) in computer-aided detection of clustered microcalcifications (MCs) in mammograms. In this work, we investigate whether it is feasible to improve the performance in MC detection by directly exploiting the FPs associated with linear structures. We analyze the cause of FPs by linear structures and their characteristics with an SVM detector, and design a linear structure detection procedure together with a dual-thresholding scheme to separate the linear structures from other tissue background in a mammogram. The proposed procedure was demonstrated on a set of 200 mammograms containing clustered MCs. The results show that it could effectively reduce the FPs in the SVM detector by as much as 30% with the true detection rate at 85%.
Case-adaptive classification based on image retrieval for computer-aided diagnosis

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.
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 extensively using the 4D gated mathematical cardiac-torso (gMCAT) Dl.Ql phantom to simulate gated SPECT perfusion imaging with Tc99m. Our experimental results demonstrate that the use of motion compensation for reconstruction can lead to significant improvement in image quality and reconstruction accuracy.
Improving uniformity in detection performance of clustered microcalcifications in mammograms

Due to variability among different subjects, the detection accuracy of microcalcifications (MC) in mammograms often varies greatly from case to case. Even for a well-developed MC detector, its performance can be hampered by a number of factors ranging from imaging noise to inhomogeneity in the breast tissue. To address this issue, we use a Bayes' risk approach to account for the variability in the detector output, wherein the probability model of the false-positives (FPs) is determined directly from the case under consideration. In the experiment, we demonstrated the proposed approach on a set of 408 mammograms. The results show that it could both improve the uniformity in detection accuracy among different cases and reduce the FP rate by as much as 44.16% with true-positive rate at 85%.
Energy-efficient feedback tracking on embedded smart cameras by hardware-level optimization

Embedded systems have limited processing power, memory and energy. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. In this paper, we introduce two methodologies to increase the energy-efficiency and battery-life of an embedded smart camera by hardware-level operations when performing object detection and tracking. The CITRIC platform is employed as our embedded smart camera. First, down-sampling is performed at hardware level on the micro-controller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, instead of performing object detection and tracking on whole image, we first estimate the location of the target in the next frame, form a search region around it, then crop the next frame by using the HREF and VSYNC signals at the micro-controller of the image sensor, and perform detection and tracking only in the cropped search region. Thus, the amount of data that is moved from the image sensor to the main memory at each frame is optimized. Also, we can adaptively change the size of the cropped window during tracking depending on the object size. Reducing the amount of transferred data, better use of the memory resources, and delegating image down-sampling and cropping tasks to the micro-controller on the image sensor, result in significant decrease in energy consumption and increase in battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection and tracking in cropped regions provide 41.24% decrease in energy consumption, and 107.2% increase in battery-life. Compared to performing software-level down-sampling and processing whole frames, proposed methodology provides an additional 8 hours of continuous processing on 4 AA batteries, increasing the lifetime of the camera to 15.5 hours.
A Distributed Outdoor Video Surveillance System for Detection of Abnormal People Trajectories

Distributed surveillance systems are nowadays widely adopted to monitor large areas for security purposes. In this paper, we present a complete multicamera system designed for people tracking from multiple partially overlapped views and capable of inferring and detecting abnormal people trajectories. Detection and tracking are performed by means of background suppression and an appearance-based probabilistic approach. Objects' label ambiguities are geometrically solved and the concept of "normality" is learned from data using a robust statistical model based on Von Mises distributions. Abnormal trajectories are detected using a first-order Bayesian network and, for each abnormal event, the appearance of the subject from each view is logged. Experiments demonstrate that our system can process with real-time performance up to three cameras simultaneously in an unsupervised setup and under varying environmental conditions.
Visions for Augmented Cultural Heritage Experience

Museum visitor experiences differ from person to person, from cognitive to affective experiences. Progress in information technology has provided us with the opportunity to improve both the quantity and personalization of cultural information, privileging the cognitive experience against the affective. Computer vision promises to be an extraordinary enabling technology for augmenting visitor experiences, bridging the affective gap by understanding the visitor's individual cognitive needs and interests and his or her situational affective state.
Entry edge of field of view for multi-camera tracking in distributed video surveillance

Efficient solution to people tracking in distributed video surveillance is requested to monitor crowded and large environments. This paper proposes a novel use of the entry edges of field of view (E/sup 2/oFoV) to solve the consistent labeling problem between partially overlapped views. An automatic and reliable procedure allows obtaining the homographic transformation between two overlapped views, without any manual calibration of the cameras. Through the homography, the consistent labeling is established each time a new track is detected in one of the cameras. A camera transition graph (CTG) is defined to speed up the establishment process by reducing the search space. Experimental results prove the effectiveness of the proposed solution also in challenging conditions.
Annotation Collection and Online Performance Evaluation for Video Surveillance: The ViSOR Project

This paper presents the Visor (video surveillance online repository) project designed with the aim of establishing an open platform for collecting, annotating, retrieving, sharing surveillance videos, and of evaluating the performance of automatic surveillance systems. The main idea is to exploit the collaborative paradigm spreading in the web community to join together the ontology based annotation and retrieval concepts and the requirements of the computer vision and video surveillance communities. The ViSOR open repository is based on a reference ontology which integrates many concepts, also coming from LSCOM and MediaMill ontologies.The web interface allows video browse, query by annotated concepts or by keywords, compressed video preview, media download and upload. The repository contains metadata annotations, which can be either manually created as ground truth or automatically generated by video surveillance systems. Their automatic annotations can be compared each other or with the reference ground-truth exploiting an integrated on-line performance evaluator.
Content-based video adaptation with user's preferences

We present an integrated system that has been designed to support automatic semantic extraction of highlights in sports video and automatic video adaptation according to user's preferences. To analyze the user's satisfaction, we propose a new performance measure that explicitly takes into account the user's preferences and considers the number and type of errors produced by the annotation engine and the way in which these errors affect the compressed video quality and bandwidth allocation. We provide experimental results with application to soccer and swimming.
Recognizing and Presenting the Storytelling Video Structure With Deep Multimodal Networks

In this paper, we propose a novel scene detection algorithm which employs semantic, visual, textual, and audio cues. We also show how the hierarchical decomposition of the storytelling video structure can improve retrieval results presentation with semantically and aesthetically effective thumbnails. Our method is built upon two advancements of the state of the art: first is semantic feature extraction which builds video-specific concept detectors; and second is multimodal feature embedding learning that maps the feature vector of a shot to a space in which the Euclidean distance has task specific semantic properties. The proposed method is able to decompose the video in annotated temporal segments which allow us for a query specific thumbnail extraction. Extensive experiments are performed on different data sets to demonstrate the effectiveness of our algorithm. An in-depth discussion on how to deal with the subjectivity of the task is conducted and a strategy to overcome the problem is suggested.
Automatic configuration and calibration of modular sensing floors

Sensing floors are becoming an emerging solution for many privacy-compliant and large area surveillance systems. Many research and even commercial technologies have been proposed in the last years. Similarly to distributed camera networks, the problem of calibration is crucial, specially when installed in wide areas. This paper addresses the general problem of automatic calibration and configuration of modular and scalable sensing floors. Working on training data only, the system automatically finds the spatial placement of each sensor module and estimates threshold parameters needed for people detection. Tests on several training sequences captured with a commercial sensing floor are provided to validate the method.
Detecting objects, shadows and ghosts in video streams by exploiting color and motion information

Many approaches to moving object detection for traffic monitoring and video surveillance proposed in the literature are based on background suppression methods. How to correctly and efficiently update the background model and how to deal with shadows are two of the more distinguishing and challenging features of such approaches. This work presents a general-purpose method for segmentation of moving visual objects (MVO) based on an object-level classification in MVO, ghosts and shadows. Background suppression needs the background model to be estimated and updated: we use motion and shadow information to selectively exclude from the background model MVO and their shadows, while retaining ghosts. The color information (in the HSV color space) is exploited to shadow suppression and, consequently, to enhance both MVO segmentation and background update.
Assessing Temporal Coherence for Posture Classification with Large Occlusions

In this paper we present a people posture classification approach especially devoted to cope with occlusions. In particular, the approach aims at assessing temporal coherence of visual data over probabilistic models. A mixed predictive and probabilistic tracking is proposed: a probabilistic tracking maintains along time the actual appearance of detected people and evaluates the occlusion probability; an additional tracking with Kalman prediction improves the estimation of the people position inside the room. Probabilistic Projection Maps (PPMs) created with a learning phase are matched against the appearance mask of the track. Finally, an Hidden Markov Model formulation of the posture corrects the frame-by-frame classification uncertainties and makes the system reliable even in presence of occlusions. Results obtained over real indoor sequences are discussed.