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Article
Peer Reviewed

Geometry processing of conventionally produced mouse brain slice images

UC Irvine Previously Published Works (2018)

Background

Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss.

New method

In this paper, we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions.

Results

Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as one of the application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain.

Comparison with existing method(s)

To the best of our knowledge the presented work is the first that automatically registers both clean as well as highly damaged high-resolutions histological slices of mouse brain to a 3D annotated reference atlas space.

Conclusions

This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data.

Article
Peer Reviewed

Geometry processing of conventionally produced mouse brain slice images.

UC Irvine Previously Published Works (2018)

Background

Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss.

New method

In this paper, we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions.

Results

Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as one of the application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain.

Comparison with existing method(s)

To the best of our knowledge the presented work is the first that automatically registers both clean as well as highly damaged high-resolutions histological slices of mouse brain to a 3D annotated reference atlas space.

Conclusions

This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data.

Creative Commons 'BY' version 4.0 license

Article
Peer Reviewed

Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes

UC Irvine Previously Published Works (2018)

Multi-spectral imaging using a camera with more than three channels is an efficient method to acquire and reconstruct spectral data and is used extensively in tasks like object recognition, relighted rendering, and color constancy. Recently developed methods are used to only guide content-dependent filter selection where the set of spectral reflectances to be recovered are known a priori. We present the first content-independent spectral imaging pipeline that allows optimal selection of multiple channels. We also present algorithms for optimal placement of the channels in the color filter array yielding an efficient demosaicing order resulting in accurate spectral recovery of natural reflectance functions. These reflectance functions have the property that their power spectrum statistically exhibits a power-law behavior. Using this property, we propose power-law based error descriptors that are minimized to optimize the imaging pipeline. We extensively verify our models and optimizations using large sets of commercially available wide-band filters to demonstrate the greater accuracy and efficiency of our multi-spectral imaging pipeline over existing methods.

Cover page: Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes

Article
Peer Reviewed

Point light fields for point rendering systems

ICS Technical Reports (2003)

In this paper, we introduce the concept of point light fields for point-based rendering systems, analogous to surface light fields for polygonal rendering systems. We evaluate two representations, namely, singular value decomposition and spherical harmonics for their representation accuracy, storage efficiency, and real-time reconstruction of point light fields. We have applied our algorithms to both real-world and synthetic point based models. We show the results of our algorithm using an advanced point based rendering system.

Cover page: Point light fields for point rendering systems

Article
Peer Reviewed

Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes.

UC Irvine Previously Published Works (2018)

Multi-spectral imaging using a camera with more than three channels is an efficient method to acquire and reconstruct spectral data and is used extensively in tasks like object recognition, relighted rendering, and color constancy. Recently developed methods are used to only guide content-dependent filter selection where the set of spectral reflectances to be recovered are known a priori. We present the first content-independent spectral imaging pipeline that allows optimal selection of multiple channels. We also present algorithms for optimal placement of the channels in the color filter array yielding an efficient demosaicing order resulting in accurate spectral recovery of natural reflectance functions. These reflectance functions have the property that their power spectrum statistically exhibits a power-law behavior. Using this property, we propose power-law based error descriptors that are minimized to optimize the imaging pipeline. We extensively verify our models and optimizations using large sets of commercially available wide-band filters to demonstrate the greater accuracy and efficiency of our multi-spectral imaging pipeline over existing methods.

Article
Peer Reviewed

Single-strips for fast interactive rendering

UC Irvine Previously Published Works (2006)

Representing a triangulated two manifold using a single triangle strip is an NP-complete problem. By introducing a few Steiner vertices, recent works find such a single-strip, and hence a linear ordering of edge-connected triangles of the entire triangulation. In this paper, we extend previous results [10] that exploit this linear ordering in efficient triangle-strip management for high-performance rendering. We present new algorithms to generate single-strip representations that follow different user defined constraints or preferences in the form of edge weights. These functional constraints are application dependent. For example, normal-based constraints can be used for efficient rendering after visibility culling, or spatial constraints for highly coherent vertex-caching. We highlight the flexibility of this approach by generating single-strips with preferences as arbitrary as the orientation of the edges. We also present a hierarchical single-strip management strategy for high-performance interactive 3D rendering.

Cover page: Single-strips for fast interactive rendering

Article
Peer Reviewed

Visualizing flat spacetime: Viewing optical versus special relativistic effects

UC Irvine Previously Published Works (2007)

A visual representation of Minkowski spacetime appropriate for a student with a background in geometry and algebra is presented. Minkowski spacetime can be modeled with a Euclidean four-space to yield accurate visualizations as predicted by special relativity. The contributions of relativistic aberration as compared to classical prerelativistic aberration to the geometry are discussed in the context of its visual representation. (C) 2007 American Association of Physics Teachers.

6 supplemental videos
1 supplemental file

Cover page: Visualizing flat spacetime: Viewing optical versus special relativistic effects

Article
Peer Reviewed

Brain image data processing using collaborative data workflows on Texera.

UC Irvine Previously Published Works (2024)

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called Texera. The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.

Cover page: Brain image data processing using collaborative data workflows on Texera.