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One of the first steps of computer-aided systems is robustly detect the anatomical boundaries. Literature has several successful energy minimization based algorithms which are applied to medical images. However, these algorithms depend on parameters which need to be tuned for a meaningful solution. One of the important parameters is the regularization parameter (λ) which is generally estimated in...
Accurate segmentation is an important preprocessing step for measuring the internal deformation of the tongue during speech and swallowing using 3D dynamic MRI. In an MRI stack, manual segmentation of every 2D slice and time frame is time-consuming due to the large number of volumes captured over the entire task cycle. In this paper, we propose a semi-automatic segmentation workflow for processing...
During the monitoring of pharmacoresistant epilepsy patients prior to surgery, interictal epileptic discharges (IEDs) are analyzed to locate possible sources of epileptic activity. In order to compensate low spatial resolution of EEG, simultaneous EEG-fMRI recordings can be used. Conventional methods typically deploy an EEG-informed analysis of the fMRI data; i.e., EEG-derived IED onset timings are...
We introduce a novel method for utilizing user input to sparsely label membranes in electron microscopy images. Using gridlines as guides, the user marks where the guides cross the membrane to generate a sparsely labeled image. We use a best path algorithm to connect each of the sparse membrane labels. The resulting segmentation has a significantly better Rand error than automatic methods while requiring...
We propose a new algorithm for classification that merges classification with reject option with classification using contextual information. A reject option is desired in many image-classification applications requiring a robust classifier and when the need for high classification accuracy surpasses the need to classify the entire image. Moreover, our algorithm improves the classifier performance...
Recently, it has been shown that interior tomography problems in x-ray CT can be uniquely determined if tiny subregions inside of the region of interest are known. The solution can be obtained by the projection onto convex sets (POCS) combined with the backprojection filtration algorithm. However, it is well-known that the convergence speed of POCS is slow; hence, to overcome the limitation, this...
In this paper, we present algorithms for the computation of the median of a set of symmetric positive-definite matrices using different distances/divergences. The novelty of this paper lies in the median computation using the Bhattacharya distance on diffusion tensors. The numerical computation of the median is achieved using the gradient descent algorithm and the fixed point algorithm. We present...
Recent denoising methods that exploit the low-rank property and sparsity of the underlying signals have produced impressive empirical results in various imaging applications. However, the fundamental limits of their denoising capability have not been systematically analyzed. This paper presents an analysis of the denoising effects of imposing low-rank and sparsity constraints. Specifically, we use...
Successful completion of dynamic cellular processes ranging from intracellular transport to embryonic development depends critically on precise spatial and temporal coordination of the molecules and cells involved. Understanding spatiotemporal behaviors of these molecules and cells is therefore essential to understanding mechanisms of their associated cellular processes. Over the past two decades,...
Object detection and classification are key tasks in computer vision that can facilitate high-throughput image analysis of microscopy data. We present a set of local image descriptors for three-dimensional (3D) microscopy datasets inspired by the well-known Haar wavelet framework. We add orientation, illumination and scale information by assuming that the neighborhood surrounding points of interests...
This paper presents a novel approach for diffeomorphic image regression and atlas estimation that results in improved convergence and numerical stability. We use a vector momenta representation of a diffeomorphism's initial conditions instead of the standard scalar momentum that is typically used. The corresponding variational problem results in a closed-form update for template estimation in both...
Segmentation via atlas registration is a common technique in medical image analysis. Devising estimates of such segmentation outcome has been of interest in cases with multiple atlases, both for single-atlas selection and for multi-atlas fusion. This paper studies the estimation of expected Dice's similarity metric for registering atlas-target pairs, by employing registration loops with models of...
The popular NL-means denoising algorithm proposes to modify the intensity of each voxel of an image by a weighted sum of the intensities of similar voxels. The success of the NL-means rests on the fact that there are typically enough such similar voxels in natural, and even medical images; in other words, that there is some self-similarity/redundancy in such images. However, similarity between voxels...
Accurate segmentation of the 30+ subcortical structures in MR images of whole diseased brains is challenging due to inter-subject variability and complex geometry of brain anatomy. However a clinically viable solution yielding precise segmentation of the structures would enable: 1) accurate, objective measurement of structure volumes many of which are associated with diseases such as Alzheimer's,...
Cerebrovascular atlases can be used to improve medical tasks requiring the analysis of 3D angiographic data. The generation of such atlases remains however a complex and infrequently considered issue. The existing approaches rely on information exclusively related to the vessels. We alternatively investigate a new way, consisting of using both vascular and morphological information (i.e., cerebral...
This paper reports a new structural approach for automated classification of histopathological tissue images. It has two main contributions: First, unlike previous structural approaches that use a single graph for representing a tissue image, it proposes to obtain a set of subgraphs through graph walking and use these subgraphs in representing the image. Second, it proposes to characterize subgraphs...
Tumor growth models based on the Fisher Kolmogorov reaction-diffusion equation (FK) have shown convincing results in reproducing and predicting the invasion patterns of gliomas brain tumors. Diffusion tensor images (DTIs) were suggested to model the anisotropic diffusion of tumor cells in the brain white matter. However, clinical patient-DTIs are expensive and often acquired with low resolution, which...
The wall thickness is known as a valuable measure for the cardiac diagnosis. From the geometric point of view, it can be considered as a function defined on the 2D manifold of the medial surface. This paper presents a novel classification method based on medial representation to diagnose and detect the myopathic regions on the left ventricle. A shape space is proposed and constructed based on the...
High-resolution, multimodal microscopy grants an intimate view of the inner workings of cells. Complex processes like cell division can be monitored with microscope images, assuming identification of cells and their cell-cycle markers: cellular structures indicative of cell-cycle progress. Here, we explore how spatial relationships between these markers can facilitate their identification. We grew...
It is well proven that the functional electrophysiological behavior of in-vitro neuronal networks is influenced by the structural connectivity. Thus, the automatic extraction of the topology in large assemblies of interconnected neurons can be a valuable tool for investigating the basic mechanisms underlying high-level cognitive functions. In this paper we propose a method for estimating the structural...
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