Hierarchical discriminative framework for detecting tubular structures in 3D images.

Detecting tubular structures such as airways or vessels in medical images is important for diagnosis and surgical planning. Many state-of-the-art approaches address this problem by starting from the root and progressing towards thinnest tubular structures usually guided by image filtering techniques. These approaches need to be tailored for each application and can fail in noisy or low-contrast regions.

Automated analysis of investigational near-infrared fluorescence lymphatic imaging in humans.

ALFIA (Automated Lymphatic Function Imaging Analysis), an algorithm providing quantitative analysis of investigational near-infrared fluorescence lymphatic images, is described. Images from nine human subjects were analyzed for apparent lymphatic propagation velocities and propulsion periods using manual analysis and ALFIA. While lymphatic propulsion was more easily detected using ALFIA than with manual analysis, statistical analyses indicate no significant difference in the apparent lymphatic velocities although ALFIA tended to calculate longer propulsion periods.

Graph based interactive detection of curve structures in 2D fluoroscopy.

An accurate and robust method to detect curve structures, such as a vessel branch or a guidewire, is essential for many medical imaging applications. A fully automatic method, although highly desired, is prone to detection errors that are caused by image noise and curve-like artifacts. In this paper, we present a novel method to interactively detect a curve structure in a 2D fluoroscopy image with a minimum requirement of human corrections.

Cross-modality assessment and planning for pulmonary trunk treatment using CT and MRI imaging.

Congenital heart defect is the primary cause of death in newborns, due to typically complex malformation of the cardiac system. The pulmonary valve and trunk are often affected and require complex clinical management and in most cases surgical or interventional treatment. While minimal invasive methods are emerging, non-invasive imaging-based assessment tools become crucial components in the clinical setting.

Discriminative distance functions and the patient neighborhood graph for clinical decision support.

There are two essential reasons for the slow progress in the acceptance of clinical similarity search-based decision support systems (DSSs); the especial complexity of biomedical data making it difficult to define a meaningful and effective distance function and the lack of transparency and explanation ability in many existing DSSs. In this chapter, we address these two problems by introducing a novel technique for visualizing patient similarity with neighborhood graphs and by considering two techniques for learning discriminative distance functions. We present an experimental study and discuss our implementation of similarity visualization within a clinical DSS.

Shape regression machine and efficient segmentation of left ventricle endocardium from 2D B-mode echocardiogram.

We present a machine learning approach called shape regression machine (SRM) for efficient segmentation of an anatomic structure that exhibits a deformable shape in a medical image, e.g., left ventricle endocardial wall in an echocardiogram.

Robust height estimation of moving objects from uncalibrated videos.

This paper presents an approach for video metrology. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene based upon tracking moving objects that primarily lie on a ground plane. Using geometric properties of moving objects, a probabilistic model is constructed for simultaneously grouping trajectories and estimating vanishing points.

Neighborhood graph and learning discriminative distance functions for clinical decision support.

There are two essential reasons for the slow progress in the acceptance of clinical case retrieval and similarity search-based decision support systems; the especial complexity of clinical data making it difficult to define a meaningful and effective distance function on them and the lack of transparency and explanation ability in many existing clinical case retrieval decision support systems. In this paper, we try to address these two problems by introducing a novel technique for visualizing inter-patient similarity based on a node-link representation with neighborhood graphs and by considering two techniques for learning discriminative distance function that help to combine the power of strong "black box" learners with the transparency of case retrieval and nearest neighbor classification.

Appearance modeling using a geometric transform.

A general transform, called the geometric transform (GeT), that models the appearance inside a closed contour is proposed. The proposed GeT is a functional of an image intensity function and a region indicator function derived from a closed contour. It can be designed to combine the shape and appearance information at different resolutions and to generate models invariant to deformation, articulation, or occlusion.

Shape regression machine.

We present a machine learning approach called shape regression machine (SRM) to segmenting in real time an anatomic structure that manifests a deformable shape in a medical image. Traditional shape segmentation methods rely on various assumptions. For instance, the deformable model assumes that edge defines the shape; the Mumford-Shah variational method assumes that the regions inside/outside the (closed) contour are homogenous in intensity; and the active appearance model assumes that shape/appearance variations are linear.

Appearance characterization of linear Lambertian objects, generalized photometric stereo, and illumination-invariant face recognition.

Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface.

From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space.

This paper addresses the problem of characterizing ensemble similarity from sample similarity in a principled manner. Using reproducing kernel as a characterization of sample similarity, we suggest a probabilistic distance measure in the reproducing kernel Hilbert space (RKHS) as the ensemble similarity. Assuming normality in the RKHS, we derive analytic expressions for probabilistic distance measures that are commonly used in many applications, such as Chernoff distance (or the Bhattacharyya distance as its special case), Kullback-Leibler divergence, etc.

Image-based face recognition under illumination and pose variations.

We present an image-based method for face recognition across different illuminations and poses, where the term image-based means that no explicit prior three-dimensional models are needed. As face recognition under illumination and pose variations involves three factors, namely, identity, illumination, and pose, generalizations in all these three factors are desired. We present a recognition approach that is able to generalize in the identity and illumination dimensions and handle a given set of poses.

Visual tracking and recognition using appearance-adaptive models in particle filters.

We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance.