https://surfer.nmr.mgh.harvard.edu/ftp/articles/desikan06-parcellation.pdf

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

https://bic.berkeley.edu/sites/default/files/Rossi_09_TMS%20safety%20review.pdf

Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research

The minimal preprocessing pipelines for the Human Connectome Project.

http://ee.sharif.edu/~miap/Files/A%20Survey%20of%20Medical%20Image%20Registration.pdf

A survey of medical image registration.

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

/pdf/mutual-information-based-registration-of-medical-images-a-4xrv2limmo.pdf

Mutual-information-based registration of medical images: a survey

We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian model to connect three elements: low-level visual features, simple "atomic" activities, and multi-agent interactions. Atomic activities are modeled as distributions over low-level visual features, and interactions are modeled as distributions over atomic activities. Our models improve existing language models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP) by modeling interactions without supervision. Our data sets are challenging video sequences from crowded traffic scenes with many kinds of activities co-occurring. Our approach provides a summary of typical atomic activities and interactions in the scene. Unusual activities and interactions are found, with natural probabilistic explanations. Our method supports flexible high-level queries on activities and interactions using atomic activities as components.

/pdf/unsupervised-activity-perception-by-hierarchical-bayesian-5ggustttxm.pdf

Unsupervised Activity Perception by Hierarchical Bayesian Models

In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from long-term observations of moving objects in the scene. First, we introduce two novel similarity measures for comparing trajectories in far-field visual surveillance. The measures simultaneously compare the spatial distribution of trajectories and other attributes, such as velocity and object size, along the trajectories. They also provide a comparison confidence measure which indicates how well the measured image-based similarity approximates true physical similarity. We also introduce novel clustering algorithms which use both similarity and comparison confidence. Based on the proposed similarity measures and clustering methods, a framework to learn semantic scene models by trajectory analysis is developed. Trajectories are first clustered into vehicles and pedestrians, and then further grouped based on spatial and velocity distributions. Different trajectory clusters represent different activities. The geometric and statistical models of structures in the scene, such as roads, walk paths, sources and sinks, are automatically learned from the trajectory clusters. Abnormal activities are detected using the semantic scene models. The system is robust to low-level tracking errors.

/pdf/learning-semantic-scene-models-by-trajectory-analysis-2c4eq8eflj.pdf

Learning semantic scene models by trajectory analysis

In recent years, the language model Latent Dirichlet Allocation (LDA), which clusters co-occurring words into topics, has been widely applied in the computer vision field. However, many of these applications have difficulty with modeling the spatial and temporal structure among visual words, since LDA assumes that a document is a "bag-of-words". It is also critical to properly design "words" and "documents" when using a language model to solve vision problems. In this paper, we propose a topic model Spatial Latent Dirichlet Allocation (SLDA), which better encodes spatial structures among visual words that are essential for solving many vision problems. The spatial information is not encoded in the values of visual words but in the design of documents. Instead of knowing the partition of words into documents a priori, the word-document assignment becomes a random hidden variable in SLDA. There is a generative procedure, where knowledge of spatial structure can be flexibly added as a prior, grouping visual words which are close in space into the same document. We use SLDA to discover objects from a collection of images, and show it achieves better performance than LDA.

/pdf/spatial-latent-dirichlet-allocation-1gsr4vnlbi.pdf

Spatial Latent Dirichlet Allocation

We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an affine congealing framework with an information theoretic objective function and is optimized via a gradient-based stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and real images.

/pdf/efficient-population-registration-of-3d-data-3dlc2k5jeu.pdf

Eric Grimson

Papers

Unsupervised Activity Perception by Hierarchical Bayesian Models

Learning semantic scene models by trajectory analysis

Spatial Latent Dirichlet Allocation

Efficient population registration of 3d data

Mutual information in coupled multi-shape model for medical image segmentation