Morphological area attribute openings and closings\cite{Cheng92,Vincent93a} are connected set
operations that are able to remove parts of an image that meet certain
criteria without changing the rest of the image. This article describes a number of
classes that implement morphological area attribute operations using
the methods described by Meijster and Wilkinson \cite{meijster02}.

Grayscale morphological attribute operations

ITK's flood filling iterator represents a way to visit pixels/voxels within an image having a specific connectivity. The iterator is initialized at known seed points and starting from these, neighbors that are within the desired connectivity are marked as to be visited and processed by the iterator in the future.

The FloodFilledFunctionConditionalConstIterator builds the foundation for the implementation of the NeighborhoodConnectedImageFilter and is currently fixed to investigating 4-neighborhoods in 2D and 6-neighborhoods in 3D. Since many existing applications use region growing algorithms that perform on full neighborhoods (i.e. 8-connected in 2D and 26-connected in 3D) it is desirable to be able to choose at least between these two standard connectivities.

In this document we describe the extension of the existing iterator to be using ShapedNeighborhoodIterators which has already been proposed in the implementation of the FloodFilledFunctionConditionalConstIterator.

Shaped Neighborhood based Flood Filled Conditional Iterators

This document describes the implementation of several features previously developed[2], extending the
2D scale invariant feature transform (SIFT)[4, 5] for images of arbitrary dimensionality, such as 3D
medical image volumes and time series, using ITK1. Specifically, we provide a scale invariant implementation
of a weighted histogram of gradient feature, a rotationally invariant version of the histogram
feature, and a SIFT-like feature, adapted to handle images of arbitrary dimensionality. This paper is accompanied
with the source code, example input data, parameters and output data, allowing reproduction
of the example results in this paper and the results previously reported[2]. Note that usage of SIFT in the
United States is governed by US Patent 6,711,293.

Scale Invariant Feature Transform for n-Dimensional Images (n-SIFT)

Convolution is a basic operation in image analysis and the Insight Toolkit provides a well-framed mechanism for such operations (see Chapter 11 of the ITK Software Guide. The classes associated with this contribution are meant to simplify convolution operations for the user who wishes to convolve a given image with an arbitrary image kernel. This entails the use of the neighborhood iterator coupled with a image kernel neighborhood operator which we provide. A couple of examples are provided to illustrate its use.

Image Kernel Convolution

A novel platform for realizing software prototypes in the area of medical image processing, analysis and visualization is introduced: The \"Medical Image Processing Platform\" (MIPP). A novel feature is the combination of ITK/VTK with Java/Eclipse RCP, four potential technologies complementing each other. While ITK and VTK provide comprehensive functionality for image processing and visualization, Java and Eclipse RCP facilitate the development of a modern and rich application.

RCP offers everything for creating plug-in based software. The benefits of integrating ITK and VTK in such a modular architecture are reusability and extensibility. Functionality usually found in software for medical image processing and visualization (2D and 3D viewers, DICOM and application data management, etc.) is provided by the MIPP platform as plug-ins, can be easily reused by new applications and needn\'t be re-implemented. Developers can extend the platform\'s features by contributing new plug-ins and, again, make these available for others.

The paper describes the architecture of the MIPP platform and outlines its most important functionality. Finally, three clinical software systems implemented with MIPP are presented.

MIPP: A Medical Image Processing Platform based on ITK, VTK and Eclipse RCP

Connected component is a well known and very useful notion in binary case. As often in mathematical morphology, this notion can be extended to the grayscale case, and allow to perform lot of the useful transforms based on binary connected components in the grayscale images. This article describe the component tree, a data structure able to ef?ciently represent the grayscale connected components in an grayscale image, as well as the algorithm used to build the component tree. All the source codes are provided, as well as a full set of tests and several usage examples of the new classes.

Component tree: an efficient representation of grayscale connected components

FusionViewer is an open source and platform independent viewer that has been specifically designed for PET/CT image display. The combination of PET and CT images offers complementary functional and anatomical information. The application (FusionViewer) facilitates efficient visualization and analysis of PET/CT studies via different viewing modes (linked cursor display, alpha-blend mode, checkerboard mode and split window mode). FusionViewer is implemented in Java and uses the Java OpenGL (JOGL) library and the Insight Segmentation and Registration Toolkit (ITK) library, which make it both a fast and cross-platform application. Its intuitive graphical user interface makes it easy to be used by physicians, radiologists, and research scientists. Several analysis and display tools are already available (navigator, zoom, pan, screen snapshot, ROI, and line measure tool; alpha-blending, checkerboard display, and split window display). Along with PET/CT, several other modalities where co-registered images are often visualized simultaneously have benefited from the use of this software.

FusionViewer: An Open Source Toolkit for Viewing Multimodality Images

This paper describes the ITK implementation of a minimal path extraction framework based on Fast Marching arrival functions. The method requires the user to provide three inputs: 1. a meaningful speed function to generate an arrival function, 2. path information in the form of start, end, and way-points (which the path must pass near), and 3. an optimizer which steps along the resultant arrival function perpendicular to the Fast Marching front. A number of perspectives for choosing speed functions and optimizers are given, as well as examples using synthetic and real images.

Fast Marching Minimal Path Extraction in ITK

This document describes a simple helper class intended for making easy to initialize the grid parameters of a BSplineDeformableTranform. This paper is accompanied with the source code, input data, parameters and output data that we used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.

Helper class for initializing the grid parameters of a BSpline deformable transform by using an image as reference

ly in ITK, the only way to find the contour of the objects in a binary image is to use the BinaryErodeImageFilter, with a kernel of radius 1. This filter is a generic filter, made to support any shape and size of structuring element, and thus is not optimized for the particular case needed to detect the contours. Moreover, that filter is not multithreaded, so it can't get the performance improvements allowed by the multiprocessor systems. As a result, the contour detection can be quite time consuming currently -- for example, in SignedMaurerDistanceMapImageFilter, the contour detection takes about 33% of the execution time.
This contribution comes with a new filter which highly improve the performance of the contour detection in the binary image, and a second filter which allow the detection of the countours in label images with similar performance.