Surface alignment of an elastic body using a multiresolution wavelet representation.

An algorithm for nonlinear registration of an elastic body is developed. Surfaces (outlines) of known anatomic structures are used to align all other (internal) points. The deformation field is represented with a multiresolution wavelet expansion and is modeled by the partial differential equations of linear elasticity. A hierarchical approach that reduces algorithm complexity is adopted. The performance of the algorithm is evaluated by two-dimensional alignment of sections from mouse brains located in the olfactory bulbs. The registration algorithm was guided by manually delineated contours of a subset of brain structures and validated based on another subset of brain structures. The wavelet alignment algorithm produced a twofold to fivefold improvement in accuracy over an affine (linear) alignment algorithm.

Design and implementation of software for assembly and browsing of 3D brain atlases.

Visualization software for three dimensional digital brain atlases present many challenges in design and implementation. These challenges include the design of an effective human interface, management of large data sets, display speed when slicing the data set for viewing/browsing, and the display of delineated volumes of interest (VOI). We present a software design, implementation and storage architecture that addresses these issues, allowing the user to navigate through a reconstructed volume quickly and smoothly, with an easy-to-use human interface. The software (macostat, for use with Macintosh OS) allows the user to rapidly display slices of the digital atlas at any arbitrary slicing angle, complete with delineated VOIs. The VOIs can be assigned colors of the user's choosing. The entire atlas, or selected portions, may be resliced with slices stored as individual image files, complete with delineations. These delineations may be transferred to corresponding sections of experimental materials using our analysis program (brain). The software may be obtained from the laboratory's web site: http://www.neuroterrain.org

Benign versus malignant classification of breast tumors based on the the PLSN model for the ultrasound RF echo and homomorphic filtering.

The Power-law Shot Noise (PLSN) model has been recently proposed for modeling the ultrasound radio-frequency echo. According to it, the spectrum of the in-phase/quadrature/envelope components are power-law functions. The corresponding power-law exponents were shown to possess good tissue characterization ability. A crucial step in the computation of in-phase/quadrature/envelope components is the estimation of the echo center frequency at different depths. We here propose a robust way of estimating the center frequency. We employ a well known convolutive model for the rf echo that views the echo as convolution of the tissue response and a component that represents the combined effect of the ultrasound impulse response and frequency dependent attenuation. Via low-pass filtering in the cepstrum domain, the combined ultrasonic contribution and attenuation term is extracted and used to estimate the center frequency. Furthermore, the tissue contribution is used to construct two new tissue characterization features. ROC analysis of 65 clinical ultrasound images of the breast indicates that the proposed features combined yield an area of 0.963.

Elastic 3-D alignment of rat brain histological images.

A three-dimensional wavelet-based algorithm for nonlinear registration of an elastic body model of the brain is developed. Surfaces of external and internal anatomic brain structures are used to guide alignment. The deformation field is represented with a multiresolution wavelet expansion and is modeled by the partial differential equations of linear elasticity. A progressive estimation of the registration parameters and the usage of an adaptive distance map reduce algorithm complexity, thereby providing computational flexibility that allows mapping of large, high resolution datasets. The performance of the algorithm was evaluated on rat brains. The wavelet-based registration method yielded a twofold improvement over affine registration.

ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis.

Breast cancer diagnosis through ultrasound tissue characterization was studied using receiver operating characteristic (ROC) analysis of combinations of acoustic features, patient age, and radiological findings. A feature fusion method was devised that operates even if only partial diagnostic data are available. The ROC methodology uses ordinal dominance theory and bootstrap resampling to evaluate A(z) and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an A(z) of 0.96 with a confidence interval of at a significance level of 0.05. The combined features show statistically significant improvement over prebiopsy radiological findings. These results indicate that ultrasound tissue characterization, in combination with patient record and clinical findings, may greatly reduce the need to perform biopsies of benign breast lesions.

Informatics center for mouse genomics: the dissection of complex traits of the nervous system.

In recent years, there has been an explosion in the number of tools and techniques available to researchers interested in exploring the genetic basis of all aspects of central nervous system (CNS) development and function. Here, we exploit a powerful new reductionist approach to explore the genetic basis of the very significant structural and molecular differences between the brains of different strains of mice, called either complex trait or quantitative trait loci (QTL) analysis. Our specific focus has been to provide universal access over the web to tools for the genetic dissection of complex traits of the CNS--tools that allow researchers to map genes that modulate phenotypes at a variety of levels ranging from the molecular all the way to the anatomy of the entire brain. Our website, The Mouse Brain Library (MBL; http://mbl.org) is comprised of four interrelated components that are designed to support this goal: The Brain Library, iScope, Neurocartographer, and WebQTL. The centerpiece of the MBL is an image database of histologically prepared museum-quality slides representing nearly 2000 mice from over 120 strains--a library suitable for stereologic analysis of regional volume. The iScope provides fast access to the entire slide collection using streaming video technology, enabling neuroscientists to acquire high-magnification images of any CNS region for any of the mice in the MBL. Neurocartographer provides automatic segmentation of images from the MBL by warping precisely delineated boundaries from a 3D atlas of the mouse brain. Finally, WebQTL provides statistical and graphical analysis of linkage between phenotypes and genotypes.