Coupling CRFs and Deformable Models for 3D Medical Image Segmentation

In this paper we present a hybrid probabilistic framework for 3D image segmentation, using Conditional Random Fields (CRFs) and implicit deformable models. Our 3D deformable model uses voxel intensity and higher scale textures as data-driven terms, while the shape is formulated implicitly using the Euclidean distance transform. The data-driven terms are used as observations in a 3D discriminative CRF, which drives the model evolution based on a simple graphical model. In this way, we solve the model evolution as a joint MAP estimation problem for the 3D label field of the CRF and the 3D shape of the deformable model. We demonstrate the performance of our approach in the estimation of the volume of the human tear menisci from images obtained with Optical Coherence Tomography. 1

CRF-based Segmentation of Human Tear Meniscus Obtained with Optical Coherence Tomography

ABSTRACT Tear film The dynamic variation of the human tear meniscus (tears around the eye lids) is very critical in visual function, maintenance of corneal integrity, and ocular comfort. The quantitative measuring of the tear menisci around the eyelids is though a challenging task. In our work, tear meniscus images are obtained with our custom-built Optical Coherence Tomography (OCT) and are processed using our novel segmentation method. For the latter, we use an implicit deformable model driven by a Conditional Random Field (CRF). The evolution of the model is solved as MAP estimation. The target conditional probability is decomposed using a simple graphical model, where the probability field of the pixel labels given the image observations is estimated using a discriminative CRF. Our results show that our segmentation approach successfully handles clutter and boundary ambiguities of the tear menisci, which makes our integrated system reliable for the every day medical practice

The infinite hidden Markov random field model

Hidden Markov random field (HMRF) models are widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e., the number of region clusters derived by the image segmentation procedure. Existing methods, including likelihood- or entropy-based criteria, and reversible Markov chain Monte Carlo methods, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (DP, infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori; infinite mixture models based on the original DP or spatially constrained variants of it have been applied in unsupervised image segmentation applications showing promising results. Under this motivation, to resolve the aforementioned issues of HMRF models, in this paper, we introduce a nonparametric Bayesian formulation for the HMRF model, the infinite HMRF model, formulated on the basis of a joint Dirichlet process mixture (DPM) and Markov random field (MRF) construction. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally demonstrate its advantages over competing methodologie

Blob Analysis of the Head and Hands: A Method for Deception Detection

Behavioral indicators of deception and behavioral state are extremely difficult for humans to analyze. Blob analysis, a method for analyzing the movement of the head and hands based on the identification of skin color is presented. This method is validated with numerous skin tones. A proof-of-concept study is presented that uses blob analysis to explore behavioral state identification in the detection of deception. 1