Fast and Robust Automatic Segmentation Methods for MR Images of Injured and Cancerous Tissues

Magnetic Resonance Imaging: MRI) is a key medical imaging technology. Through in vivo soft tissue imaging, MRI allows clinicians and researchers to make diagnoses and evaluations that were previously possible only through biopsy or autopsy. However, analysis of MR images by domain experts can be time-consuming, complex, and subject to bias. The development of automatic segmentation techniques that make use of robust statistical methods allows for fast and unbiased analysis of MR images. In this dissertation, I propose segmentation methods that fall into two classes---(a) segmentation via optimization of a parametric boundary, and: b) segmentation via multistep, spatially constrained intensity classification. These two approaches are applicable in different segmentation scenarios. Parametric boundary segmentation is useful and necessary for segmentation of noisy images where the tissue of interest has predictable shape but poor boundary delineation, as in the case of lung with heavy or diffuse tumor. Spatially constrained intensity classification is appropriate for segmentation of noisy images with moderate contrast between tissue regions, where the areas of interest have unpredictable shapes, as is the case in spinal injury and brain tumor. The proposed automated segmentation techniques address the need for MR image analysis in three specific applications:: 1) preclinical rodent studies of primary and metastatic lung cancer: approach: a)),: 2) preclinical rodent studies of spinal cord lesion: approach: b)), and: 3) postclinical analysis of human brain cancer: approach: b)). In preclinical rodent studies of primary and metastatic lung cancer, respiratory-gated MRI is used to quantitatively measure lung-tumor burden and monitor the time-course progression of individual tumors. I validate a method for measuring tumor burden based upon average lung-image intensity. The method requires accurate lung segmentation; toward this end, I propose an automated lung segmentation method that works for varying tumor burden levels. The method includes development of a novel, two-dimensional parametric model of the mouse lungs and a multifaceted cost function to optimally fit the model parameters to each image. Results demonstrate a strong correlation: 0.93), comparable with that of fully manual expert segmentation, between the automated method\u27s tumor-burden metric and the tumor burden measured by lung weight. In preclinical rodent studies of spinal cord lesion, MRI is used to quantify tissues in control and injured mouse spinal cords. For this application, I propose a novel, multistep, multidimensional approach, utilizing the Classification Expectation Maximization: CEM) algorithm, for automatic segmentation of spinal cord tissues. In contrast to previous methods, my proposed method incorporates prior knowledge of cord geometry and the distinct information contained in the different MR images gathered. Unlike previous approaches, the algorithm is shown to remain accurate for whole spinal cord, white matter, and hemorrhage segmentation, even in the presence of significant injury. The results of the method are shown to be on par with expert manual segmentation. In postclinical analysis of human brain cancer, access to large collections of MRI data enables scientifically rigorous study of cancers like glioblastoma multiforme, the most common form of malignant primary brain tumor. For this application, I propose an efficient and effective automated segmentation method, the Enhanced Classification Expectation Maximization: ECEM) algorithm. The ECEM algorithm is novel in that it introduces spatial information directly into the classical CEM algorithm, which is otherwise spatially unaware, with low additional computational complexity. I compare the ECEM\u27s performance on simulated data to the standard finite Gaussian mixture EM algorithm, which is not spatially aware, and to the hidden-Markov random field EM algorithm, a commonly-used spatially aware automated segmentation method for MR brain images. I also show sample results demonstrating the ECEM algorithm\u27s ability to segment MR images of glioblastoma

Globally Optimal Cell Tracking using Integer Programming

We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generates an over-complete set of competing detection hypotheses. We then perform detection and tracking simultaneously on these hypotheses by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor

Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm

This paper addresses the problem of estimating the Potts parameter B jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on B requires computing the intractable normalizing constant of the Potts model. In the proposed MCMC method the estimation of B is conducted using a likelihood-free Metropolis-Hastings algorithm. Experimental results obtained for synthetic data show that estimating B jointly with the other unknown parameters leads to estimation results that are as good as those obtained with the actual value of B. On the other hand, assuming that the value of B is known can degrade estimation performance significantly if this value is incorrect. To illustrate the interest of this method, the proposed algorithm is successfully applied to real bidimensional SAR and tridimensional ultrasound images

Motion Segmentation from Clustering of Sparse Point Features Using Spatially Constrained Mixture Models

Motion is one of the strongest cues available for segmentation. While motion segmentation finds wide ranging applications in object detection, tracking, surveillance, robotics, image and video compression, scene reconstruction, video editing, and so on, it faces various challenges such as accurate motion recovery from noisy data, varying complexity of the models required to describe the computed image motion, the dynamic nature of the scene that may include a large number of independently moving objects undergoing occlusions, and the need to make high-level decisions while dealing with long image sequences. Keeping the sparse point features as the pivotal point, this thesis presents three distinct approaches that address some of the above mentioned motion segmentation challenges. The first part deals with the detection and tracking of sparse point features in image sequences. A framework is proposed where point features can be tracked jointly. Traditionally, sparse features have been tracked independently of one another. Combining the ideas from Lucas-Kanade and Horn-Schunck, this thesis presents a technique in which the estimated motion of a feature is influenced by the motion of the neighboring features. The joint feature tracking algorithm leads to an improved tracking performance over the standard Lucas-Kanade based tracking approach, especially while tracking features in untextured regions. The second part is related to motion segmentation using sparse point feature trajectories. The approach utilizes a spatially constrained mixture model framework and a greedy EM algorithm to group point features. In contrast to previous work, the algorithm is incremental in nature and allows for an arbitrary number of objects traveling at different relative speeds to be segmented, thus eliminating the need for an explicit initialization of the number of groups. The primary parameter used by the algorithm is the amount of evidence that must be accumulated before the features are grouped. A statistical goodness-of-fit test monitors the change in the motion parameters of a group over time in order to automatically update the reference frame. The approach works in real time and is able to segment various challenging sequences captured from still and moving cameras that contain multiple independently moving objects and motion blur. The third part of this thesis deals with the use of specialized models for motion segmentation. The articulated human motion is chosen as a representative example that requires a complex model to be accurately described. A motion-based approach for segmentation, tracking, and pose estimation of articulated bodies is presented. The human body is represented using the trajectories of a number of sparse points. A novel motion descriptor encodes the spatial relationships of the motion vectors representing various parts of the person and can discriminate between articulated and non-articulated motions, as well as between various pose and view angles. Furthermore, a nearest neighbor search for the closest motion descriptor from the labeled training data consisting of the human gait cycle in multiple views is performed, and this distance is fed to a Hidden Markov Model defined over multiple poses and viewpoints to obtain temporally consistent pose estimates. Experimental results on various sequences of walking subjects with multiple viewpoints and scale demonstrate the effectiveness of the approach. In particular, the purely motion based approach is able to track people in night-time sequences, even when the appearance based cues are not available. Finally, an application of image segmentation is presented in the context of iris segmentation. Iris is a widely used biometric for recognition and is known to be highly accurate if the segmentation of the iris region is near perfect. Non-ideal situations arise when the iris undergoes occlusion by eyelashes or eyelids, or the overall quality of the segmented iris is affected by illumination changes, or due to out-of-plane rotation of the eye. The proposed iris segmentation approach combines the appearance and the geometry of the eye to segment iris regions from non-ideal images. The image is modeled as a Markov random field, and a graph cuts based energy minimization algorithm is applied to label the pixels either as eyelashes, pupil, iris, or background using texture and image intensity information. The iris shape is modeled as an ellipse and is used to refine the pixel based segmentation. The results indicate the effectiveness of the segmentation algorithm in handling non-ideal iris images

Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.Comment: 12 pages, 4 figures, 3 table