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We present a framework for cell tracking in a highly cluttered environment in live cell imaging from mouse brain cortex. Our goal is to track cells over a long period of time for intracellular calciumion concentration in order to detect important cellular events such as neural activity and cell division. Since traditional object tracking approach such as segmentation followed by tracking is not applicable...
We report the implementation of a fully on-chip, lensless, sub-pixel resolving optofluidic microscope (SROFM) based on the super resolution algorithm. The device utilizes microfluidic flow to deliver specimens directly across a complementary metal oxide semiconductor (CMOS) sensor to generate a sequence of low-resolution (LR) projection images, where resolution is limited by the sensor's pixel size...
Among the most critical components of a computerized system for automated melanoma detection is image sampling and pooling of the extracted features. In this paper, we propose a new method for sampling and pooling based on a combination of spatial pooling and graph theory features. The performance of the new method is evaluated using a dataset of more than 1,500 images representing pigmented skin...
This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
Image segmentation is one of the key problems in medical image analysis. This paper presents a new statistical shape model for automatic image segmentation. In contrast to the previous model based segmentation methods, where shape priors are estimated from a general population-based shape model, our proposed method aims to estimate patient-specific shape priors to achieve more accurate segmentation...
This paper presents a unified framework aimed at detecting unstained living cells in bright-field (BF) microscopy and finding suitable microinjection points within their surface. Automatic localization of cells is a critical step in improving the procedure of microinjection that, so far, is still conducted manually by trained operators. This work compares different state of the art image processing...
This paper presents ongoing work towards creating a framework for the active segmentation and classification of cell assay images. In this paper we focus on the learning of a probabilistic boundary model followed by an extended segmentation method. The abilities are demonstrated on a variety of cell images. We conclude by outlining approaches for the active segmentation of cell images.
This article presents a novel photoreceptor detection algorithm applied to in-vivo Adaptive Optics (AO) images of the retina obtained from an advanced ophthalmic diagnosis device. Our algorithm is based on a recursive construction of thresholded connected components when the seeds of the recursions are the regional maxima of the image. This algorithm results in a labeling of the AO image which is...
Pleural effusions are accumulations of fluid in the pleural space, usually associated with atelectasis of the adjacent lung. We have previously presented an automated method to measure the volume of pleural effusions on chest CT images. This paper presents an improved version of the same method, which adds 3D surface modeling and additional propagation of the segmentation in the inferior direction...
This paper addresses the clinically challenging problem of hairline mandibular fracture detection from a sequence of Computed Tomography (CT) images. A hairline fracture of critical clinical importance, can be easily missed due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT image, if not scrutinized carefully. In this work, the 2D...
Motion estimation, also known as optic flow, refers to the process of determining a 2D displacement field that aligns two images. Most methods that estimate motion or deformation fields in biological image sequences rely on sparse, distinct features (landmarks). Going a step forward, we are interested in methods to compute dense deformation fields (for all pixels). In this paper we compare two of...
This paper presents a novel unsupervised vascular segmentation algorithm which is applied to retinal fundus images, however could be generalised to any two-dimensional vascular image. The algorithm presents a new fully automatic framework for vessel segmentation and comprises the following features: novel application of the NPWindows method for intensity distribution estimation on localised `image...
Retinal image analysis is currently a very vivid field in biomedical image analysis. One of the most challenging tasks is the reliable automatic detection of microaneurysms (MAs). Computer systems that aid the automatic detection of diabetic retinopathy (DR) greatly rely on MA detection. In this paper, we present a method to construct an MA score map, from which the final MAs can be extracted by simple...
We propose an automatic color segmentation system that (1) incorporates domain knowledge to guide histological image segmentation and (2) normalizes images to reduce sensitivity to batch effects. Color segmentation is an important, yet difficult, component of image-based diagnostic systems. User-interactive guidance by domain experts-i.e., pathologists-often leads to the best color segmentation or...
Plaque composition analysis is a critical tool in identifying vulnerable atherosclerotic plaques. Intravascular ultrasound with spectral analysis of the backscattered radio frequency (RF) signals (IVUS-VH) is currently considered as the gold standard for the evaluation of coronary plaque composition, while CT coronary angiography (CTA) has been proposed as a potential non-invasive counterpart. In...
Time sequences of 3D images of cerebral and other vasculature blood flow during surgery and other medical procedures allow enhanced visual feedback. The visual feedback constitutes an enhancement over the existing 2D time series of X-ray projections as it facilitates the detection and observation of pathological abnormalities such as stenoses, aneurysms, and blood clots. An algorithm that outputs...
We introduce a unifying framework for the non-local regularization of biomedical inverse problems. We choose the regularization functional as the sum of distances between pairs of patches in the image. We introduce a novel majorize minimize algorithm to minimize the proposed criterion. We observe that the first iteration of the algorithm to be very similar to the classical non-local regularization...
In this paper, we propose a task-based approach to parametric imaging and apply the proposed method to an example problem of prostate cancer segmentation with dynamic contrast enhanced Magnetic Resonance Imaging (DCE MRI). Traditionally, the time activity curve obtained from dynamic series of MR images is modeled without considering a specific task in order to obtain the kinetic parameters and to...
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...
One of the key MRI methodologies to identify and characterize coronary artery disease is dynamic contrast enhanced myocardial perfusion imaging. Rapid acquisition of images can help in improved diagnosis by accurately measuring temporal dynamics of the injected contrast agent. Another competing requirement is complete coverage of the heart with high spatial resolution to better identify sub-endocardial...
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