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Segregation and integration are two general principles of the brain's functional architecture; therefore brain network analysis is of significant importance in understanding brain function. Critical to brain network analysis and construction is the identification of reliable, reproducible and accurate network nodes, or Regions of Interest (ROIs). In this paper, based on functional ROIs derived from...
Iterative image reconstruction for positron emission tomography (PET) can improve image quality by using spatial regularization that penalizes image intensity difference between neighboring pixels. The most commonly used quadratic penalty often over-smoothes edges and small objects in reconstructed images. Non-quadratic penalties can preserve edges but may introduce piece-wise constant blocky artifacts...
We present a convex variational active contour model with shape priors, for spatio-temporal segmentation of the endocardium in 2D B-mode ultrasound sequences, which can be solved by Continuous Cuts. A four component (signal dropout, echocardiographic artifacts, blood and tissue) Rayleigh mixture model is proposed for modeling the inside and outside of the endocardium. The parameters of the mixture...
The estimation of the mid-sagittal plane (MSP) is a known problem with several applications in neuroimage analysis. As advance to the state-of-the-art, we present a considerably better approach for MSP extraction based on bilateral symmetry maximization and a more suitable error metric to compare MSP estimation methods. The proposed method was quantitatively evaluated using three other state-of-the-art...
The channelized Hotelling observer (CHO) has become a widely used approach for evaluating medical image quality, acting as a surrogate for human observers in early-stage research on assessment and optimization of imaging devices and algorithms. Its popularity stems from experiments showing that, when an internal-noise model is introduced, the CHO's detection performance can be tuned to correlate well...
Ant Colony Systems (ACS) have been applied to solve complex problems. The first Ant System was proposed in the earlier nineties, and since then several studies were performed to apply this paradigm in real problems. Several researchers have explored the idea of applying ACS to image processing. Herein, the original ACS models applied to image processing are presented. Moreover, two new models, based...
Brain image segmentation is one of the most important applications in medicine and also is one of the most challenging topics in the field of medical image processing. In general, most automatic segmentation methods consist of an energy function, a shape model, and an optimization strategy. Each plays an important role in the design of an accurate segmentation algorithm. Here we introduce a modified...
Filamentary structures extraction in medical and biological images is a challenging problem. Muscular/Neural fibers, neurites and blood arteries are some examples. Their delineation is particularly problematic due to the lack of solid visual support that is also compromised by the presence of clutter and low signal to noise ratios. In this article, we propose a modular approach to curvilinear structures...
In medical imaging, there are many approaches for automatic segmentation. However, none of these methods provide any effective solution to correct segmentation interactively, which becomes a necessity in the case of poorly defined structures. Manual segmentation can not be an alternative given that it might be unfeasible in many cases. On the other hand, how to complete a poor automatic segmentation...
We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids...
Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
Investigating multi-feature information-theoretic image registration, we introduce consistent and asymptotically unbiased kth-nearest neighbor (kNN) estimators of mutual information (MI), normalized MI and exclusive information applicable to high-dimensional random variables, and derive under closed-form their gradient flows over finite- and infinite-dimensional transform spaces. Using these results,...
Symmetry and inverse consistency are two important features for deformable image registration in medical imaging analysis. This work presents a novel registration method computing symmetric and inverse-consistent image alignment efficiently while preserving high accuracy and consistency of the mapping. This is achieved by optimizing a symmetric energy functional estimating forward and backward transformations...
Bioluminescence imaging (BLI) offers an alternative opportunity for non-invasively visualizing biological processes at the physiological and molecular levels in whole animals. Tomographic bioluminescence imaging (TBI) can further translate planar imaging into three-dimensional quantitative bioluminescent source distribution. Although many reconstruction methods have been developed, efforts are still...
The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, a hybrid deconvolution method is employed to recover a reliable estimate of the tissue reflectivity function directly from ultrasound RF data. Here, the “hybridization” suggests a two-stage reconstruction scheme, in...
The degree of white matter (WM) myelination is rather inhomogeneous across the brain. As a consequence, white matter appears differently across the cortical lobes in MR images acquired during early postnatal development. At 1 year old specifically, the gray/white matter contrast of MR images in prefrontal and temporal lobes is limited and thus tissue segmentation results show commonly reduce accuracy...
We have developed a hybrid system for imaging small animals using fluorescence optical tomography (FOT) and positron emission tomography (PET) simultaneously. This paper presents a statistical method for reconstructing spatial distribution of dual-labeled tracers from the combined PET and FOT data. We use the Poisson likelihood function for the PET data and Gaussian distribution for the FOT data....
Ant Colony Optimization (ACO) metaheuristic is a recent population-based approach inspired by the observation of real ants colony and based upon their collective foraging behavior. In This paper, the proposed technique ACO hybrid with Fuzzy segmentation. In the first step, the MRI brain image is Segmented Aco Hybrid with Fuzzy method to extract the suspicious region. In the second step deals with...
In this paper, a novel approach to MRI Brain Image segmentation based on the Hybrid Parallel Ant Colony Optimization (HPACO) with Fuzzy C-Means (FCM) Algorithm have been used to find out the optimum label that minimizes the Maximizing a Posterior (MAP) estimate to segment the image. There are M colonies, M-1 colonies treated as slaves and one colony for master. Each colonies visit all the pixels with...
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