Image projection is a very common task in image analysis to reduce the dimension of an image. A base filter is provided, with some specialized filters which implement different projection methods.

Image projections along an axis

This document describes itk::ContourExtractor2DImageFilter, an implementation of the arching squares2D contour-finding method using the Insight Toolkit ITK www.itk.org. This filter takes as input a 2D image (not necessarily binarized) and a contour value, and outputs zero or more itk::PolyLineParametricPath objects which represent the image contours at that value.

ContourExtractor2DImageFilter: A subpixel-precision image isocontour extraction filter.

The DICOM Working Group 4 (compression group) has approved in November 2001 the use of JPEG 2000 compression as part of the DICOM standard. This document describes how this wavelet transform-based image compression algorithms is now integrated via GDCM and OpenJPEG in the Insight Toolkit ITK.

JPEG 2000 in Medical Applications

I have made some changes to make itk::PathToImageFilter to make it a usable base class. There were 
some problems with the CVS version, including: (1) this ase classhad actual filter functionality, (2) 
the mechanism for specifying the size, spacing, origin, etc., of the output image was incomplete, contrary 
to ITK convention, and hard to extend, and (3) the documentation specified that the filter would calculate 
the size of the output image from the path, but it did not. 

I solved these problems by (1) moving the filter functionality to itk::TracePathOnImageFilter, (2) 
requiring that filter->GetOutput()->Set...(...) should be used to set the information for the 
output image, and (3) providing methods to calculate the correct image region a path inhabits, and a 
boolean flag to specify whether that calculation should be done (default true).

Changes to itk::PathToImageFilter.

The report details some modifications made to the ITK
{\em ConnectedComponentImageFilter} in an attempt to improve
performance. Some interesting observations were made during this
process. A new filter using a different algorithm to perform the same
function is also described and improved performance demonstrated.

Optimization of connected component labelling

Morphological reconstruction may be implemented in a number of
different ways. ITK has an iterative method and a non iterative
method. This article compares the performance of another non iterative
method and finds a significant improvement.

Improving performance of morphological reconstruction

Fast computation of distance transforms find direct application
in various computer vision problems. Currently there exists 
two image filters in the ITK library which can be used to generate
distance maps. Unfortunately, these 
filters produce only approximations to the Euclidean Distance
Transform (EDT). We introduce into the ITK library a third EDT 
filter which was developed by Maurer {\em et al.} \cite{Maurer2003}.
In contrast to other algorithms, this algorithm produces the 
exact signed squared EDT using integer arithmetic. The complexity,
which is formally verified, is $O(n)$ with a small time constant
where $n$ is the number of image pixels.

N-D Linear Time Exact Signed Euclidean Distance Transform

The itk::GaussianDerivativeImageFunction computes the derivative at the specified physical or pixel location. Unfortunately it has a number of deficiencies, for which I suggest possible solutions.

A small rework for the Gaussian Derivative Image Function

LabelOverlayImageFilter colorize a label image and put it on top of the input image.

Label overlay

Binary erosion and dilation are the base filters of binary mathematical morphology.
In ITK 2.4.1 \verb$BinaryDilateImageFilter$ and \verb$BinaryErodeImageFilter$ implement
an efficient algorithm described in \cite{Nikopoulos2000}. However, those filter lack support for boundary values, have a poor progress report, and can be quite inefficient
for complex 3D images.