Pairwise likelihood estimation for multivariate mixed Poisson models generated by Gamma intensities

Estimating the parameters of multivariate mixed Poisson models is an important problem in image processing applications, especially for active imaging or astronomy. The classical maximum likelihood approach cannot be used for these models since the corresponding masses cannot be expressed in a simple closed form. This paper studies a maximum pairwise likelihood approach to estimate the parameters of multivariate mixed Poisson models when the mixing distribution is a multivariate Gamma distribution. The consistency and asymptotic normality of this estimator are derived. Simulations conducted on synthetic data illustrate these results and show that the proposed estimator outperforms classical estimators based on the method of moments. An application to change detection in low-flux images is also investigated

Change detection in multisensor SAR images using bivariate gamma distributions

This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of the paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors

A Comparative Evaluation of Different Techniques of Supervised Classification in Landuse/Landcover Mapping of Awka South L.G.A, Anambra State, Nigeria.

The aim of this study is to compare the different techniques of supervised classification using Awka South LGA, of Anambra State as a case study. The techniques considered include: Maximum Likelihood (MLC), Minimum Distance, Mahalanobis Distance, Spectral Angle Mapper and Parallelepiped. Landsat 7 ETM+ (2000 and 2007) and Landsat 8 OLI/TIRS (2015) were acquired. The images were pre-processed. The scan-line effect present in the Landsat 7 image was corrected using the analysis tool of Quantum GIS (QGIS) 2.18 software. To compensate for atmospheric effects, Fast Line-of-site Atmospheric Analysis of Hypercube (FLAASH) Atmospheric Module of ENVI software was used. Image enhancement was carried out on the images. The images were classified using the different techniques and the results compared. Change detection was also carried out to determine the rate of changes between 2000 and 2015. Error matrices of the various techniques were calculated to determine the accuracy level of the algorithms and to judge which is the better choice. It can be deduced from the results that Maximum Likelihood (99.63%) produced the best result, followed closely by Mahalanobis Distance (98.54%), Spectral Angle (89.28%), Minimum Distance (84.42%) and Parallelepiped (85.00%). The study recommends Maximum Likelihood Classification algorithm for supervised classification. Key words: Classification, Maximum Likelihood, Algorithm, Land cover land use DOI: 10.7176/JEES/9-5-10 Publication date:May 31st 201

The effect of lossy image compression on image classification

We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network

Maximum-Likelihood Estimation of Glandular Fraction for Mammography and its Effect on Microcalcification Detection

Objective: Breast tissue is a mixture of adipose and fibro-glandular tissue. The risk of undetected breast cancer increases with the amount of glandular tissue in the breast. Therefore, radiologists need to know quantitative glandular fraction when diagnosing a patient. Another increasingly popular mammography protocol is eliminating the anti-scatter grid and using software algorithms to reduce scatter. This work uses a Maximum Likelihood Expectation Maximization algorithm to estimate the pixel-wise glandular fraction from images taken with an anti-scatter grid or with scatter removed algorithmically. The work also studies if presenting the pixel-wise glandular fraction image alongside the usual mammography image has the potential to improve micro-calcification detection. Approach: The algorithms are implemented and evaluated with TOPAS Geant4-generated images with known glandular fractions. These images are also taken with and without microcalcifications present to study the effects of GF-estimation on microcalcification detection. We then applied the algorithm to a few clinical DICOM images with and without microcalcifications. Results: For the TOPAS simulated images, the glandular fraction was estimated with a root mean squared error of 3.2% and 2.5% for the without and with anti-scatter grid cases. Results from DICOM clinical images (where the proper glandular fraction is unknown) show that the algorithm gives a glandular fraction within the average range expected from the literature. For microcalcification detection, the contrast-to-noise ratio improved by 17.5-548% in DICOM images and 5.1-88% in TOPAS images. Significance: This work studied the accuracy of maximum likelihood estimation for a glandular fraction on simulated and clinical images and shows an improvement in contrast to noise ratio for detecting microcalcifications, a risk factor in breast cancer.Comment: Manuscipt under peer-revie

Maximum-likelihood detection of sources among Poissonian noise

A maximum likelihood (ML) technique for detecting compact sources in images of the x-ray sky is examined. Such images, in the relatively low exposure regime accessible to present x-ray observatories, exhibit Poissonian noise at background flux levels. A variety of source detection methods are compared via Monte Carlo, and the ML detection method is shown to compare favourably with the optimized-linear-filter (OLF) method when applied to a single image. Where detection proceeds in parallel on several images made in different energy bands, the ML method is shown to have some practical advantages which make it superior to the OLF method. Some criticisms of ML are discussed. Finally, a practical method of estimating the sensitivity of ML detection is presented, and is shown to be also applicable to sliding-box source detection.Comment: 17 pages, 10 figures. Accepted by Astronomy & Astrophysic

Edge and Line Feature Extraction Based on Covariance Models

age segmentation based on contour extraction usually involves three stages of image operations: feature extraction, edge detection and edge linking. This paper is devoted to the first stage: a method to design feature extractors used to detect edges from noisy and/or blurred images. The method relies on a model that describes the existence of image discontinuities (e.g. edges) in terms of covariance functions. The feature extractor transforms the input image into a “log-likelihood ratio” image. Such an image is a good starting point of the edge detection stage since it represents a balanced trade-off between signal-to-noise ratio and the ability to resolve detailed structures. For 1-D signals, the performance of the edge detector based on this feature extractor is quantitatively assessed by the so called “average risk measure”. The results are compared with the performances of 1-D edge detectors known from literature. Generalizations to 2-D operators are given. Applications on real world images are presented showing the capability of the covariance model to build edge and line feature extractors. Finally it is shown that the covariance model can be coupled to a MRF-model of edge configurations so as to arrive at a maximum a posteriori estimate of the edges or lines in the image