Parametric optimization of imaging system based on minimization of image distortion

The technique of discriminant analysis is widely used for discriminating arson from nonarson structures. Since in this particular case only two groups are involved viz., the arson and the match groups, the computations required for obtaining a discriminant function are relatively simple. Because of this, it is possible to delete a variable by examining the value of a sub i d sub i where a sub i is the coefficient for the i-th variable in the discriminant function and d sub i is the difference between the means for the arson and the match groups for the i th variable (i=1, 2, ... , p). Thus, if a sub i d sub i is less than 0 then we delete the i th variable from consideration because inclusion of such a varable, increases the probability of misclassification. A prior knowledge of a i d i is particularly useful when we have large numbers of variables to be considered. This will help to reduce the number of discriminant functions one needs to consider in order to arrive at the discriminant function which minimizes the misclassification probability. Frequently, in developing a discriminant function one strives to increase the probability of correct classification. However, this probability reflects two components, viz., the probability of correct classification of known arson structures and the probability of correct classification of known nonarson structures. Even if the overall probability of correct classification is high, it does not necessarily imply, as the illustrations of Boston and New York City indicate, that the resulting discriminant function is also efficient in the sense that more arson cases are correctly classified than the nonarson cases. To this end, a procedure has been suggested to compute the cost of the wrong decision for a discriminant function which can be used to compare the cost of two similar types of discriminant functions. Using the relative cost efficiency criterion, it has been shown that the discriminant function for Newark is more efficient than the discriminant function for either Boston or New York City.

New instruments currently in design for Earth observational remote sensing from space offer promising advancements in the use of remote sensing for soil studies. Due to the enormous quantities of data generated by these high spectral resolution sensors, efficient use will only be feasible if new algorithms are developed to compress the data while maintaining information content. A spectral band selection algorithm is proposed to identify the important spectral bands for compression and classification of soil reflectance data. This algorithm was developed from the shape dominancy concept of Karhunen‐Loeve based optimal features. The high dimensional raw data were first transformed into much lower dimensionality by the derived spectral features. Canonical analysis was then used to transform the data under the maximal separability criterion into their final signal space where the data classification was performed. Soil data sets with and without stratification by soil order and climatic moisture zone were used to test the algorithm. The probabilities of correct classification of soil organic matter content using Landsat Multispectral Scanner (MSS) bands, Thematic Mapper (TM) bands, and the bands identified using the new band selection algorithm, were calculated and compared. The algorithm was successful in finding important spectral bands for soil organic matter content classification. The probabilities of correct classification obtained by using these bands were 0.850 and 0.883 for unstratified data sets, as compared to 0.600 to 0.640 for MSS bands and 0.640 to 0.653 for TM bands. Probabilities of correct classification for climate‐stratified data ranged from 0.910 to 0.980 using the calculated bands. The overall data compression ratio achieved using the algorithm was greater than 10 with no loss in classification accuracy.

To present an effective classification method based on the prevalence of Schistosoma mansoni in the community. We created decision rules (defined by cut-offs for number of positive slides), which account for imperfect sensitivity, both with a simple adjustment of fixed sensitivity and with a more complex adjustment of changing sensitivity with prevalence. To reduce screening costs while maintaining accuracy, we propose a pooled classification method. To estimate sensitivity, we use the De Vlas model for worm and egg distributions. We compare the proposed method with the standard method to investigate differences in efficiency, measured by number of slides read, and accuracy, measured by probability of correct classification. Modelling varying sensitivity lowers the lower cut-off more significantly than the upper cut-off, correctly classifying regions as moderate rather than lower, thus receiving life-saving treatment. The classification method goes directly to classification on the basis of positive pools, avoiding having to know sensitivity to estimate prevalence. For model parameter values describing worm and egg distributions among children, the pooled method with 25 slides achieves an expected 89.9% probability of correct classification, whereas the standard method with 50 slides achieves 88.7%. Among children, it is more efficient and more accurate to use the pooled method for classification of S. mansoni prevalence than the current standard method.

Using Monte Carlo methods, the relationship is investigated between the actual probability of correct classification using the calculated linear discriminant function and the estimate of this probability which can be easily obtained by estimating the Miahalanobis distance between the two populations. Graphs are given for obtaining conservative confidence intervals for the probabilities of correct classification, given the estimated probability. For a given number of variates used to calculate the discriminant function the estimate of the probability becomes more satisfactory in terms of length of confidence interval as the size of the sample increases. For a given sample size, the estimate of the probability becomes less satisfactory as the number of variates is increased from two to ten. Tables with several percentiles in the distribution of these ratios are also included. Data for graphs and tables were obtained by Monte Carlo methods.

This paper describes a performance metric for the evaluation of active coherent imaging systems. This metric can be determined for any system using analytical considerations or measured using standard targets. It has implications for comparison of different imaging systems, optimization of imaging systems, and identification of areas in which significant improvements in particular systems can be realized. The imaging system performance metric described here is suitable for analysis of unconventional imaging systems in which the image is sensed by an array of discrete detectors or in which the image is produced by manipulation of arrays of data. The implications of this formula for determination of photon-efficient and optimized systems are discussed. An important result of this paper will be to show that the efficient use of photons is only part of the story. An efficient system must still be optimized to make best use of the imaging hardware.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The fusion of multiple Computer Aided Detection/Computer Aided Classification (CAD/CAC) algorithms has been shown to be effective in reducing the false alarm rate associated with the automated classification of bottom mine-like objects when applied to side-scan sonar images taken in Very Shallow Water (VSW) environments [C.M. Ciany et al., 2001],[C.M. Ciany et al., 2000]. The fusion of CAD/CAC algorithms from Raytheon and NSWC Coastal Systems Station (CSS) also has been demonstrated in the shallow water environment on a single sonar data set [C.M. Ciany et al., 2002]. This paper extends the shallow water CAD/CAC/Fusion performance analysis to an additional set of sonar data taken in the Gulf of Mexico during June 1998, and adds the outputs of a third CAD/CAC algorithm from Lockheed Martin to the fusion processing. The fusion algorithm accepts the classification confidence levels and associated contact locations from the three different CAD/CAC algorithms, clusters the contacts based on the distance between their locations, and then declares a valid target when a clustered contact passes a prescribed fusion criterion. Four different fusion criteria are evaluated: the first based on the Fisher Discriminant, the second and third based on simple and constrained binary combinations of the multiple CAD/CAC processor outputs, and the fourth based on a constrained optimization approach that minimizes the total number of false alarms over the clustering distance and cluster confidence factor thresholds for a given probability of correct classification. The resulting performance of the four fusion algorithms is compared, and the overall performance benefit of a significant reduction of false alarms at high correct classification probabilities is quantified.

An important variant of the classification problem can be formulated as follows: given a learning sample (supervised learning) a classifier shall be determined, whose probability of correct classification is a good approximation of the maximal (Bayesian) correct classification probability. If there is no parametric familiy available which encloses the class-specific distributions of the observable, then the statistician is forced to apply non-parametric procedures (e.g. density estimators).

BackgroundMost studies of epidemic detection focus on their start and rarely on the whole signal or the end of the epidemic. In some cases, it may be necessary to retrospectively identify outbreak signals from surveillance data. Our study aims at evaluating the ability of change point analysis (CPA) methods to locate the whole disease outbreak signal. We will compare our approach with the results coming from experts’ signal inspections, considered as the gold standard method.MethodsWe simulated 840 time series, each of which includes an epidemic-free baseline (7 options) and a type of epidemic (4 options). We tested the ability of 4 CPA methods (Max-likelihood, Kruskall-Wallis, Kernel, Bayesian) methods and expert inspection to identify the simulated outbreaks. We evaluated the performances using metrics including delay, accuracy, bias, sensitivity, specificity and Bayesian probability of correct classification (PCC).ResultsA minimum of 15 h was required for experts for analyzing the 840 curves and a maximum of 25 min for a CPA algorithm. The Kernel algorithm was the most effective overall in terms of accuracy, bias and global decision (PCC = 0.904), compared to PCC of 0.848 for human expert review.ConclusionsFor the aim of retrospectively identifying the start and end of a disease outbreak, in the absence of human resources available to do this work, we recommend using the Kernel change point model. And in case of experts’ availability, we also suggest to supplement the Human expertise with a CPA, especially when the signal noise difference is below 0.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0271-x) contains supplementary material, which is available to authorized users.

Detection of Mastitis in Dairy Cattle by Use of Mixture Models for Repeated Somatic Cell Scores: A Bayesian Approach via Gibbs Sampling

Calculation of the probability of correct classification in probabilistic bagged k-Nearest Neighbours

Infrared cameras are sometimes not fast or sensitive enough when short events with low temperature dynamics have to be measured. Thermal imaging systems sensitive at 3micrometers - 5micrometers usually operate with integration times of 1 ms and more for room temperature scene measurements. Thus very short events with low dynamics cannot be resolved with sufficient temporal and thermal resolution. Advanced measurement techniques which make use of triggering, summing or even lock-in must be used then. We present an infrared imaging system, based on a high quantum efficiency 384x288 pixel HgCdTe FPA detector, for temperature measurements of gasoline sprays ejected out of injection nozzles for automobile motors. The temperature distribution of the gasoline jet while ejected out of a injection nozzle (the process is finished after 2 ms) is urged to be known with high accuracy at high temporal and spatial resolution. With highly advanced instrumentation we are able to measure with both high temporal and temperature resolution. The system described here helps automotive engineers to better understand and improve the combustion process in modern motors.

The ideal observer is widely used in imaging system optimization. One practical question remains open: do the ideal and human observers have the same preference in system optimization and evaluation? Based on the ideal observer's mathematical properties proposed by Barrett et. al. and the empirical properties of human observers investigated by Myers et. al., I attempt to pursue the general rules regarding the applicability of the ideal observer in system optimization. Particularly, in software optimization, the ideal observer pursues data conservation while humans pursue data presentation or perception. In hardware optimization, the ideal observer pursues a system with the maximum total information, while humans pursue a system with the maximum selected (e.g., certain frequency bands) information. These different objectives may result in different system optimizations between human and the ideal observers. Thus, an ideal observer optimized system is not necessarily optimal for humans. I cite empirical evidence in search and detection tasks, in hardware and software evaluation, in X-ray CT, pinhole imaging, as well as emission computed tomography to corroborate the claims. (<i>Disclaimer: the views expressed in this work do not necessarily represent those of the FDA</i>)

In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistical estimation. The...

Performance of automatic target recognition (ATR) systems depends on numerous factors including the mission description, operating conditions, sensor modality, and ATR algorithm itself. Performance prediction models sensitive to these factors could be applied to ATR algorithm design, mission planning, sensor resource management, and data collection design for algorithm verification. Ideally, such a model would return measures of performance (MOPs) such as probability of detection (Pd), correct classification (Pc), and false alarm (Pfa), all as a function of the relevant predictor variables. Here we discuss the challenges of model-based and data-based approaches to performance prediction, concentrating especially on the synthetic aperture radar (SAR) modality. Our principal conclusion for model-based performance models (predictive models derived from fundamental physics- and statistics-based considerations) is that analytical progress can be made for performance of ATR system components, but that performance prediction for an entire ATR system under realistic conditions will likely require the combined use of Monte Carlo simulations, analytical development, and careful comparison to MOPs from real experiments. The latter are valuable for their high-fidelity, but have a limited range of applicability. Our principal conclusion for data-based performance models (that fit empirically derived MOPs) offer a potentially important means for extending the utility of empirical results. However, great care must be taken in their construction due to the necessarily sparse sampling of operating conditions, the high-dimensionality of the input space, and the diverse character of the predictor variables. Also the applicability of such models for extrapolation is an open question.

BackgroundNear-infrared (NIR) fluorescent sentinel lymph node (SLN) mapping in breast cancer requires optimized imaging systems and lymphatic tracers.MethodsA small, portable version of the FLARE™ imaging system, termed Mini-FLARE™, was developed for capturing color video and two semi-independent channels of NIR fluorescence (700 nm and 800 nm) in real-time. Initial optimization of lymphatic tracer dose was performed using 35-kg Yorkshire pigs and a 6-patient pilot clinical trial. More refined optimization was performed in twenty-four consecutive breast cancer patients. All patients received the standard of care using 99mTechnetium-nanocolloid and patent blue. In addition, 1.6 mL of indocyanine green adsorbed to human serum albumin (ICG:HSA) was injected directly after patent blue at the same location. Patients were allocated to one of eight escalating ICG:HSA concentration groups from 50 to 1000 μM.ResultsThe Mini-FLARE™ system was positioned easily in the operating room and could be used up to 13″ from the patient. Mini-FLARE™ enabled visualization of lymphatic channels and SLNs in all patients. A total of 35 SLNs (mean = 1.45, range 1–3) were detected: 35 radioactive (100%), 30 blue (86%), and 35 NIR fluorescent (100%). Contrast agent quenching at the injection site and dilution within lymphatic channels were major contributors to signal strength of the SLN. Optimal injection dose of ICG:HSA ranged between 400 and 800 μM. No adverse reactions were observed.ConclusionsWe describe the clinical translation of a new NIR fluorescence imaging system and define the optimal ICG:HSA dose range for SLN mapping in breast cancer.