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Cell segmentation in microscopy imagery is essential for many biomedical applications. In this paper, a novel level set based technique is proposed for segmenting the differential interference contrast (DIC) red blood cell microscopy images. Based on the framework of subjective surfaces, a local complex phase based edge indicator function is introduced to replace the traditional gradient-based edge...
We present the results of a study to determine the sensitivity of biomarkers in in vivo brain MRS signals to post-acquisitional processing algorithms and parameters. Using a comprehensive integrated suite of post-processing and inference algorithms (BIDASCA) we examine the impact of different parameter values for model-based water suppression on the identification of statistically significant wavelet-based...
Segmentation and tracking of tagged MR images is a critical component of in vivo understanding for the heart dynamics. In this paper, we propose a novel approach which uses multi-dimensional features and casts the left ventricle (LV) extraction problem as a maximum posteriori estimation process in both the feature and the shape spaces. Exact integration of multi-dimensional boundary and regional statistics...
In order to more accurately detect drusen and vessel from retinal fundus images, we proposed a set of new features and presented a learning based detection scheme. These features are designed to describe the variation patterns of image's local geometrical structure across various scales. Theoretical analysis and a series of preliminary experimental results demonstrate the extra ability and high accuracy...
Reliable detection of large retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. In this study, we propose a novel large retinal hemorrhages detection method based on splat feature classification. Fundus photographs are partitioned into a number of splats covering the entire image. Each splat contains pixels with similar color and...
In diffusion magnetic resonance imaging (dMRI), the Ensemble Average Propagator (EAP), also known as the propagator, describes completely the water molecule diffusion in the brain white matter without any prior knowledge about the tissue shape. In this paper, we describe a new and efficient method to accurately reconstruct the EAP in terms of the Spherical Polar Fourier (SPF) basis from very few diffusion...
Automatic classification of cancer lesions for gastroenterology imaging scenarios poses novel challenges to computer assisted decision systems, owing to their distinct visual characteristics such as reduced color spaces or natural organic textures. In this paper, we explore the prospects of using Gabor filters in a texton framework for the classification of images from two distinct imaging modalities...
The ability to accurately interpret large image scenes is often dependent on the ability to extract relevant contextual, domain-specific information from different parts of the scene. Traditionally, techniques such as multi-scale (i.e. multi-resolution) frameworks and hierarchical classifiers have been used to analyze large images. In this paper we present a novel framework that classifies entire...
Introduction of automated methods for heart function assessment have the potential to minimize the variance in operator assessment. This paper considers automated classification of rest and stress echocardiography. One previous attempt has been made to combine information from rest and stress sequences utilizing a Hidden Markov Model (HMM), which has proven to be the best performing approach to date...
Segmentation of tree-like structure within medical imaging modalities, such as x-ray, MRI, ultrasound, etc., is an important step for analyzing branching patterns involved in many anatomic structures. However, images acquired using these different acquisition techniques frequently have features of poor contrast, blurring and noise, and therefore the segmentation result of traditional image segmentation...
White matter hyperintensities (WMH) are commonly seen on T2-weighted images in elderly people. They are considered as a potential marker of vascular pathology and have been associated with motor and cognitive deficits. In this paper, non linear diffusion was applied to FLAIR images and combined with precise anatomical knowledge extracted from T1-weighted images to automatically segment WMH. Evaluation...
With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data available for disease diagnosis and prognosis, there is a need for quantitative tools to combine such varied channels of information, especially imaging and non-imaging data (e.g. spectroscopy, proteomics). The major problem in such quantitative data integration lies in reconciling the large spread in the range of dimensionalities...
Autism severely impairs personal behavior and communication skills, so that improved diagnostic methods are called for. Neuropathological studies have revealed abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We explore a possibility of distinguishing between autistic and normal (control) brains by quantitative CC shape analysis in the 3D magnetic resonance images (MRI). Our approach...
Automated computational tools are needed to estimate the position of a slice of interest within a contiguous stack of slices. Such estimation is useful to retrieve relevant slices from a volume of slices in clinical analysis or it can be used as an initialization step to other post-processing and image analysis techniques. In this paper, we present a novel method to determine the location of a slice...
Computed Tomography Angiography (CTA) of the heart is a non-invasive procedure to rule out coronary artery disease or measure its extent and plan treatments and interventions. The need for coronary tree tracking methods that require minimum human interaction and produce accurate and robust measurements is therefore of great clinical importance. In this work we present a probabilistic coronary artery...
Pancreas segmentation in 3-D computed tomography (CT) data is of high clinical relevance, but extremely difficult since the pancreas is often not visibly distinguishable from the small bowel. So far no automated approach using only single phase contrast enhancement exist. In this work, a novel fully automated algorithm to extract the pancreas from such CT images is proposed. Discriminative learning...
Extracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using a measure derived from the Kullback-Leibler...
This contribution presents a method for automatic detection of excitatory, asymmetric synapses and segmentation of synaptic junctional complexes in stacks of serial electron microscopy images with nearly isotropic resolution. The method uses a Random Forest classifier in the space of generic image features, computed directly in the 3D neighborhoods of each pixel, and an additional step of interactive...
Searching for vertebrae in a large collection of spine X-ray images that are relevant to pathology is potentially important for providing assistance to radiologists and bone morphometrists. Developing appropriate methods for such searching tasks is very challenging due to the high similarities among vertebral shapes in contrast to the subtle dissimilarities that characterize the pathology. In this...
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