As part of the Center of Excellence in Genomic Science at Caltech, we have initiated the Digital Fish
Project. Our goal is to use in toto imaging of developing transgenic zebrafish embryos on a genomic scale
to acquire digital, quantitative, cell-based, molecular data suitable for modelling the biological circuits
that turn an egg into an embryo. In toto imaging uses confocal/2-photon microscopy to capture the
entire volume of organs and eventually whole embryos at cellular resolution every few minutes in living
specimens thoughout their development. The embryos are labelled such that nuclei are one color and cell
membranes another color to allow cells to be segmented and tracked as they move and divide. The use
of a transgenic marker in a third color allows a variety of molecular data to be marked. In toto imaging
generates 4-d image sets (xyzt) which can contain 100,000 to 1,000,000 images per experiment. We are
developing a software package called GoFigure to visualize, segment, and analyze these very large image
sets. GoFigure uses a MySQL database back end for managing storage of images and segmented objects
and uses VTK and ITK for visualization and segmentation. We plan to use in toto imaging to digitize the
complete expression and subcellular localization patterns of thousands of proteins throughout zebrafish
embryogenesis. This genomic data, our zebrafish lines, and GoFigure will be distributed following the
Open Data/Open Source model.

GoFigure and The Digital Fish Project: Open tools and open data for an imaging based approach to system biolgy

This paper describes a new open-source tutorial for image-guided therapy (IGT) and medical robotics that is both accessible and hands-on using the LEGO Mindstorms NXT (a commercially available robotics kit) and 3D Slicer (an open-source application for medical image processing). The tutorial covers all stages of a typical IGT or medical robotics procedure, including the concepts of imaging, preoperative planning, targeting and tracking, navigation and registration, by using the LEGO robot to perform a “needle biopsy” on a phantom (anatomical model) made of traditional LEGO pieces. In addition, this paper describes a C++ library that allows direct control of a LEGO Mindstorms NXT robot from a Linux computer over a USB connection.

An accessible, hands-on tutorial system for image-guided therapy and medical robotics using a robot and open-source software

This document describes work-in-progress for refactoring ITK’s registration methods to exploit the parallel, computation power of multi-processor, shared-memory systems. Refactoring includes making the methods multi-threaded as well as optimizing the algorithms. API backward compatibility is being maintained. Helper classes that solve common registration tasks are also being developed.


The refactoring has reduced computation times by factors of 2 to 200 for metrics, interpolators, and transforms computed on multi-processor systems. Extensive sets of tests are being developed to further test operation and backward compatibility.

Optimizing ITK’s Registration Methods for Multi-processor, Shared-Memory Systems

In this work, 
we extend a previously demonstrated entropy based groupwise registration method
to include a non-rigid deformation model based on B-splines. 
We describe an open source implementation of the groupwise registration algorithm 
using the Insight Toolkit ITK.
We provide the source code, parameters, input and
output data that we used for validation.


We describe an efficient implementation of the algorithm 
by using a stochastic optimization scheme embedded in a multi-resolution setting.
The objective function is optimized using gradient descent algorithm combined
with line search for the step size.
The derivative of the objective function is evaluated efficiently by computing Jacobian of 
B-spline deformation field locally.


We demonstrate the algorithm in application to different imaging modalities
including proton density, FA, T1 and T2 MR images. 
We validate the algorithm on synthetic datasets varying from 2 to 30 images
by recovering randomly applied affine and B-spline transforms.

Non-rigid Groupwise Registration using B-Spline Deformation Model

It is sometimes useful to apply a filter to a region defined by a
mask. This article introduces a simple ``mask adaptor'' filter that
allows this type of operation to be tested easily. It is applicable to filters that do not need any pixel location information, such as histogram operations, which can't necessarily be achieved by simple masking approaches.

Image mask adaptor for pixel order independent filters

In this paper we present a generalization of Frangi's vesselness measure \cite{Frangi1998} for 
the enhacement of M-dimensional shapes in N-dimensional images, together 
with the implementation of the corresponding Insight Toolkit (ITK) filter \cite{ITKSoftwareGuide}. Inspired 
by the implementation of a multiscale vesselness measure recently presented on 
the Insight Journal \cite{Enquobahrie2007}, we also propose a unified framework for the evaluation of 
generic multiscale Hessian-based measures. The manuscript is accompanied by 
source code and examples.

Generalizing vesselness with respect to dimensionality and shape

Richard Beare has recently introduced a new filter to efficiently labelize the connected components with ITK, and has also proposed to use the run-length encoding used in that filter to implement some of the most useful binary mathematical morphology operators: the opening by attribute. Following that idea, and after have searched a way to use the ITK's spatial objects for this task, a new set of classes have been developed to represent and manipulate the label images and the objects within them in ITK.
Those new classes have been used to implement several label images manipulation based on object attributes, as well as the binary and label specialization of some mathematical morphology filters based on the morphological reconstruction. This contribution comes with 65 new classes, and should greatly enhance the binary mathematical
morphology in ITK.

All the source codes are provided, as well as a full set of tests and several usage examples of the new classes.


Label object representation and manipulation with ITK

We present an open-source ITK implementation of a direct Fourier method for tomographic reconstruction, applicable to parallel-beam x-ray images. Direct Fourier reconstruction makes use of the central-slice theorem to build a polar 2D Fourier space from the 1D transformed projections of the scanned object, that is resampled into a Cartesian grid. Inverse 2D Fourier transform eventually yields the reconstructed image. Additionally, we provide a complex wrapper to the BSplineInterpolateImageFunction to overcome ITK’s current lack for image interpolators dealing with complex data types. A sample application is presented and extensively illustrated on the Shepp-Logan head phantom. We show that appropriate input zeropadding and 2D-DFT oversampling rates together with radial cubic b-spline interpolation improve 2D-DFT interpolation quality and are efficient remedies to reduce reconstruction artifacts.

Direct Fourier Tomographic Reconstruction Image-To-Image Filter

This document describes an addition to the Image IO Library of the Insight Toolkit (ITK). ITK has
been able to read in Analyze image files but not the Analyze Object Maps that correspond to the images.
Analyze Object maps define regions of interest that are helpful in designating locations, giving a name to
those locations, as well as display characteristics. Also, Analyze Object Maps are useful to tell someone
where to look in an image. With the lack of not being able to read/write Analyze Object Maps, a new set
of classes have been developed to read/write Analyze Object Maps and manipulate the object maps.

Reader/Writer for Analyze Object Maps for ITK

Computation of local image derivatives is an important operation in many image processing tasks that
involve feature detection and extraction, such as edges, corners or more complicated features. However,
derivative computation in discrete images is an ill-posed problem and derivative operators without
any prior smoothing are known to enhance noise. Here we present a new convolution operator, the
GaussianDerivativeOperator, that allows to calculate locally Gaussian derivatives of N order. Furthermore,
we present some useful classes and examples that make use of this new operator.