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As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images. This problem negatively affects the performance of segmentation algorithms and little prior work has been done to correct this data. In this work, we adapt the N3 framework for intensity inhomogeneity correction, which was originally developed to correct MRI data, to work for macular OCT data. We first transform the data to a flattened macular space to create a template intensity profile for each layer giving us an accurate initial estimate of the gain field. N3 will then produce a smoothly varying field to correct the data. We show that our method is able to both accurately recover synthetically generated gain fields and improves the stability of the layer intensities.

Most large asteroid population discovery has been accomplished to date by Earth-based telescopes. It is speculated that most of the smaller Near Earth Objects (NEOs) that are less than 100 meters in diameter, whose impact can create substantial city-size damage, have not yet been discovered. Many asteroids cannot be detected with an Earth-based telescope given their size and/or their location with respect to the Sun. We are investigating the feasibility of deploying asteroid detection algorithms on-board a spacecraft, thereby minimizing the expense and need to downlink large collection of images. Having autonomous on-board image analysis algorithms enables the deployment of a spacecraft at approximately 0.7 AU heliocentric or Earth-Sun L1/L2 halo orbits, removing some of the challenges associated with detecting asteroids with Earth-based telescopes. We describe an image analysis algorithmic pipeline developed and targeted for on-board asteroid detection and show that its performance is consistent with deployment on flight-qualified hardware.

The problem of target localization with noise is addressed. The target is a sample from a continuous random variable with known distribution and the goal is to locate it with minimum mean squared error distortion. The localization scheme or policy proceeds by queries, or questions, weather or not the target belongs to some subset as it is addressed in the 20-question framework. These subsets are not constrained to be intervals and the answers to the queries are noisy. While this situation is well studied for adaptive querying, this paper is focused on the non adaptive querying policies based on dyadic questions. The asymptotic minimum achievable distortion under such policies is derived. Furthermore, a policy named the Aurelian1 is exhibited which achieves asymptotically this distortion.

We present a simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs. The user provides a partial segmentation of the organ of interest, which is used to guide the template towards its target. The user also highlights some elements of the background that should be excluded from the final segmentation. We derive by likelihood maximization a registration algorithm from a simple statistical image model in which the user labels are modeled as Bernoulli random variables. The resulting registration algorithm minimizes the sum of square differences between the binary template and the user labels, while preventing the template from shrinking, and penalizing for the inclusion of background elements into the final segmentation. We assess the performance of the proposed algorithm on synthetic images in which the amount of user annotation is controlled. We demonstrate our algorithm on the segmentation of the lungs of Mycobacterium tuberculosis infected mice from μCT images.

Many techniques have been proposed to segment organs from images, however the segmentation of diseased organs remains challenging and frequently requires lots of user interaction. The challenge consists of segmenting an organ while its appearance and its shape vary due to the presence of the disease in addition to individual variations. We propose a template registration technique that can be used to recover the complete segmentation of a diseased organ from a partial segmentation. The usual template registration method is modified in such a way that it is robust to missing parts. The proposed method is used to segment Mycobacterium tuberculosis infected lungs in CT images of experimentally infected mice. Using synthetic data, we evaluate and compare the performance of the proposed algorithm with the usual sum of squared difference cost function.

We present a generic statistical deformable model for gray level medical images and propose to use it for template matching. Template matching methods usually rely on the arbitrary choice of a cost function and a template. Statistical models, on the other hand, allow us to derive optimal learning and matching algorithms from the modeling assumptions using likelihood maximization principles. We test the statistical deformable model on the automatic anatomical landmark detection in brain MRI, and compare its performance with the sum of squared differences (SSD), a reference cost-function for intensity-based template matching.

In order to perform medical image registration, landmarks are used to settle correspondences between images. A landmark is a voxel in the image that corresponds to a well-defined point in the anatomy. Manual landmarking is a difficult, tedious and time-consuming procedure that would gain to be automated. We propose a Bayesian approach for automatic landmarking. Using training data, we learn the geometry through a probabilistic template. Landmarking consists then in estimating an affine transformation mapping the image onto the template. We use gradient ascent in the likelihood function to perform this task. Experiments validate the methodology for landmarking the temporal lobe in MR brain images.

We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model is well known from practitioners. Pixels are considered as independent. The second model is a Hidden Markov Model. It includes constraints that force smoothness of the solution. The third model is a first order model. The full color gradient is included. Parameter estimation as well as optimization cannot be tackled without approximations. We use thoroughly Bethe tree approximation of the pixel lattice. Within it , parameter estimation is eradicated and the belief propagation algorithm permits to obtain exact and fast solution for skin probability at pixel locations. We then assess the performance on the Compaq database.