Hemant D. Tagare

Quantifying the local resolution of cryo-EM density maps

We propose a definition of local resolution for three-dimensional electron cryo-microscopy (cryo-EM) density maps that uses local sinusoidal features. Our algorithm has no free parameters and is applicable to other imaging modalities, including tomography. By evaluating the local resolution of single-particle reconstructions and subtomogram averages for four example data sets, we report variable resolution across a 4- to 40-Å range.

Using Prior Shapes in Geometric Active Contours in a Variational Framework

In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the segmented contour to the prior shape. The active contour is able to find boundaries that are similar in shape to the prior, even when the entire boundary is not visible in the image (i.e., when the boundary has gaps). A level set formulation of the active contour is presented. The existence of the solution to the energy minimization is also established.We also report experimental results of the use of this contour on 2d synthetic images, ultrasound images and fMRI images. Classical active contours cannot be used in many of these images.

Medical Image Databases: A Content-based Retrieval Approach

Information contained in medical images differs considerably from that residing in alphanumeric format. The difference can be attributed to four characteristics: (1) the semantics of medical knowledge extractable from images is imprecise; (2) image information contains form and spatial data, which are not expressible in conventional language; (3) a large part of image information is geometric; (4) diagnostic inferences derived from images rest on an incomplete, continuously evolving model of normality. This paper explores the differentiating characteristics of text versus images and their impact on design of a medical image database intended to allow content-based indexing and retrieval. One strategy for implementing medical image databases is presented, which employs object-oriented iconic queries, semantics by association with prototypes, and a generic schema.

A theory of photometric stereo for a class of diffuse non-Lambertian surfaces

A theory of photometric stereo is proposed for a large class of non-Lambertian reflectance maps. The authors review the different reflectance maps proposed in the literature for modeling reflection from real-world surfaces. From this, they obtain a mathematical class of reflectance maps to which the maps belong. They show that three lights can be sufficient for a unique inversion of the photometric stereo equation for the entire class of reflectance maps. They also obtain a constraint on the positions of light sources for obtaining this solution. They investigate the sufficiency of three light sources to estimate the surface normal and the illuminant strength. The issue of completeness of reconstruction is addressed. They show that if k lights are sufficient for a unique inversion, 2k lights are necessary for a complete inversion. >

On the incorporation of shape priors into geometric active contours

A novel model for boundary determination that incorporates prior shape information into geometric active contours is presented. The basic idea of this model is to minimize the energy functional depending on the information of the image gradient and the shape of interest, so that the boundary of the object can be captured either by higher magnitude of the image gradient or by the prior knowledge of its shape. The level set form of the proposed model is also provided. We present our experimental results on some synthetic images, functional MR brain images, and ultrasound images for which the existing active contour methods are not applicable. The existence of the solution to the proposed minimization problem is also discussed.

Evaluation of Four Probability Distribution Models for Speckle in Clinical Cardiac Ultrasound Images

Segmenting cardiac ultrasound images requires a model for the statistics of speckle in the images. Although the statistics of speckle are well understood for the raw transducer signal, the statistics of speckle in the image are not. This paper evaluates simple empirical models for first-order statistics for the distribution of gray levels in speckle. The models are created by analyzing over 100 images obtained from commercial ultrasound machines in clinical settings. The data in the images suggests a unimodal scalable family of distributions as a plausible model. Four families of distributions (Gamma, Weibull, Normal, and Log-normal) are compared with the data using goodness-of-fit and misclassification tests. Attention is devoted to the analysis of artifacts in images and to the choice of goodness-of-fit and misclassification tests. The distribution of parameters of one of the models is investigated and priors for the distribution are suggested

Shape-based nonrigid correspondence with application to heart motion analysis

A common problem in many biomedical imaging studies is that of finding a correspondence between two plane curves which aligns their shapes. A mathematical formulation and solutions to this problem is proposed in this paper. The formulation exhibits desirable properties. It allows for one-to-one as well as non-one-to-one correspondences, it consistently compares shape, even in nonrigid situations, and it is completely symmetric with respect to the two curves. A numerical implementation of the algorithm for finding the optimal correspondence is also reported. The algorithm is used to estimate nonrigid motion of the endocardium in MRI image sequences of normal and post-infarct dog hearts. The return error (the difference between the starting and ending positions of a point) is used as a performance measure to evaluate the technique. Since heart motion is periodic, the return error is a measure of consistency of the algorithm. Preliminary applications to other data sets are reported as well.

Deformable 2-D template matching using orthogonal curves

A new formulation of the two-dimensional (2-D) deformable template matching problem is proposed. It uses a lower-dimensional search space than conventional methods by precomputing extensions of the deformable template along orthogonal curves. The reduction in search space allows the use of dynamic programming to obtain globally optimal solutions and reduces the sensitivity of the algorithm to initial placement of the template. Further, the technique guarantees that the result is a curve which does not collapse to a point in the absence of strong image gradients and is always nonself intersecting. Examples of the use of the technique on real-world images and in simulations at low signal-to-noise ratios (SNRs) are also provided.

A New Approach to 3D Sulcal Ribbon Finding from MR Images

Sulcal medial surfaces are 3D thin convoluted ribbons embedded in cortical sulci, and they provide distinctive anatomical features of the brain. Here we propose a new approach to automatic intrasulcal ribbon finding, following our work on cortex segmentation with coupled surfaces via level set methods, where the outer cortical surface is embedded as the zero level set of a high-dimensional distance function. Through the utilization of this distance function, we are able to formulate the sulcal ribbon finding problem as one of surface deformation, thus avoiding possible control problems in other work using sliding contour models. Using dynamic programming and deformable surface models, our method requires little manual intervention and results parameterized sulcal ribbon surfaces in nearly real-time. Though a natural follow up to our earlier segmentation work, we describe how it can be applied with general segmentation methods. We also present quantitative results on 15 MR brain images.

Medical image collection indexing: Shape-based retrieval using KD-trees

The capacity to retrieve images containing objects with shapes similar to a query shape is desirable in medical image databases. We propose a similarity measure and an indexing mechanism for non-rigid comparison of shape which adds this capability to image databases. The (dis-)similarity measure is based on the observations that: (1) the geometry of the same organ in different subjects is not related by a strictly rigid transformation; and (2) the orientation of the organ plays a key role in comparing shape. We propose a similarity measure that computes a non-rigid mapping between curves and uses this mapping to compare oriented shape. We also show how KD-trees can index curves so that retrieval with our similarity measure is efficient. Experiments with real-world data from a database of magnetic resonance images are provided.