For more accurate classification of earthquake-induced damaged regions, a high-resolution satellite image is required to extract textural and spatial features of the damage. In addition to using textural features, spectral features may improve the identification of the damaged regions. Earthquake-induced damage that occurred in the city of Bam in Iran was identified by a nonparametric and nonlinear classifier called support vector selection and adaptation (SVSA) using both the textural and the spectral features. SVSA can achieve the performance of nonlinear support vector machines (NSVM) without the need for a kernel function. Our aim is to show the effectiveness of the SVSA algorithm compared with the linear support vector machines, NSVM, and K-nearest neighbor (KNN) methods in terms of classification accuracy when using the textural features. A nonparametric weighted feature extraction was also implemented before the classification in order to increase the classification accuracy further by assigning a different weight to the textural feature. The results indicate that SVSA is significantly better than the linear SVM (LSVM) and KNN classifiers, and it is quite competitive with NSVM in terms of damage detection accuracy.
Segmentation of dynamic PET images is an important preprocessing step for kinetic parameter estimation. A single time activity curve (TAC) is extracted for each segmented region. This TAC is then used to estimate the kinetic parameters of the segmented region. Current methods perform this task in two independent steps; first dynamic positron emission tomography (PET) images are reconstructed from the
projection data using conventional tomographic reconstruction methods, then the time activity curves (TAC) of the pixels are clustered into a predetermined number of clusters. In this paper, we propose to cluster the regions of dynamic PET images directly on the projection data and simultaneously estimate the TAC of each cluster.
This method does not require an intermediate step of tomographic reconstruction for each time frame. Therefore the dimensionality of the estimation problem is reduced. We compare the proposed method with weighted least squares (WLS) and expectation maximization with Gaussian mixtures methods (GMM-EM). Filtered backprojection is used to reconstruct the emission images required by these methods.
Our simulation results show that the proposed method can substantially decrease the number of mislabeled pixels and reduce the root mean squared error (RMSE) of the cluster TACs.
Recently, there has been interest in estimating kinetic model parameters for each voxel in a PET image. To do this, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated
by fitting a model to the reconstructed time-activity response of each voxel. However, this indirect approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames. In 1985, Carson and Lange proposed, but did not
implement, a method based on the EM algorithm for direct parametric reconstruction. More recently, researchers have developed semi-direct methods which use spline-based reconstruction, or direct methods for estimation of kinetic parameters from image regions. However, direct voxel-wise parametric reconstruction has remained a challenge due to the unsolved complexities of inversion and required spatial regularization. In this work, we demonstrate an efficient method for direct voxel-wise reconstruction of kinetic parameters (as a parametric image) from all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. This PICD algorithm is computationally efficient and allows the physiologically important kinetic parameters to be spatially regularized. Our experimental simulations demonstrate that direct parametric reconstruction can substantially reduce estimation error of kinetic parameters as compared to indirect methods.
It is often necessary to analyze the time response of a tracer. A common way of analyzing the tracer time response is to use a compartment model and estimate the model parameters. The model parameters are generally physiologically meaningful and called "kinetic parameters". In this paper, we simultaneously estimate both the kinetic parameters at each voxel and the model-based plasma input function directly from the sinogram data. Although the plasma model parameters are not our primary interest, they are required for accurate reconstruction of kinetic parameters. The plasma model parameters are initialized with an image domain method to avoid local minima, and multiresolution optimization is used to perform the required reconstruction. Good initial guesses for the plasma parameters are required for the algorithm to converge to the correct answer. Therefore, we devised a preprocessing step involving clustering of the emission images by temporal characteristics to find a reasonable plasma curve that was consistent with the kinetics of the multiple tissue types. We compare the root mean squared error (RMSE) of the kinetic parameter estimates with the measured (true) plasma input function and with the estimated plasma input function.
Tests using a realistic rat head phantom and a real plasma input function show that we can simultaneously estimate the kinetic parameters of the two-tissue compartment model and plasma input function. The RMSE of the kinetic parameters increased for some parameters and remained the same or decreased for other parameters.