Polar transform is a geometric transform, that transforms points form cartesian coordinates to so-called polar coordinates. In case of 2D space, a point in polar coordinate system is addressed by radius and angle. In image processing, polar transform is usually used to convert rotations around the origin of polar coordinate system to translations. 

This submission contains the implementation of forward and inverse polar transforms in two very simple classes for the Insight Toolkit.

Polar Transforms for ITK

When an image is mostly composed of background pixels, most of the automatic
thresholding methods are failing to produce a relevant threshold. This is mainly
caused because one mode is over represented compared to the other in the histogram
of the image. The Kappa-Sigma clipping, a method largely used in astronomy, don't
try to separate 2 modes in the histogram, but rather try to find the properties of
the only usable mode, the one of the background, and compute a threshold to select
the values significantly different of the background.

Kappa Sigma Clipping

While filtering in N dimensions is a main feature of ITK, filtering an image
in N-1 dimensions, slice by slice, can be very useful in many cases. Currently,
this operation require a consequent amount of work to be done with ITK. A new
filter is provided with this article to perform this operation with only a few
lines of code.

Slice by slice filtering with ITK

Robust Automatic Threshold Selection (RATS) is a fast and noise robust
automatic thresholding method based on gradients in the image. The basic idea is to choose
the threshold at the intensity where the gradient are the highest.

Robust Automatic Threshold Selection

This paper describes a new tutorial book titled "An Introduction to Programming for Medical Image Analysis with the Visualization Toolkit." This book derived from a set of class handouts used in a biomedical engineering graduate seminar at Yale University. The goal for the seminar was to
introduce the students to the Visualization Toolkit (VTK) and, to a lesser extent, the Insight Toolkit (ITK). A draft version of the complete book (including all the sample code) is available online at www.bioimagesuite.org/vtkbook.

Medical Image Analysis with VTK: A Tutorial

Phase Correlation Method (PCM or SPOMF - Symmetric Phase-Only Matched Filter) is a well known image registration method, that exploits Fourier Shift Theorem property of Fourier Transform. Even though it is limited to estimate only shifts between two images, it is very useful as it is robust to frequency-dependent noise and initial image overlap area. Furthermore, it evaluates very fast by way of two forward FFTs, one complex image division and one inverse FFT.

Phase Correlation Method for ITK

In an earlier submission, we considered 2-D and 3-D digital binary images topologically characterized by their well-composedness. Well-composed images exhibit important topological and geometrical properties not shared by their ill-composed counterparts. These properties have important implications for various algorithms used by the ITK community such as thinning algorithms and Marching Cubes. 

A natural follow-up inquiry to our original submission concerns the possible extension of the binary algorithm to accommodate images with multiple labels. In this paper, we describe such a generalization of our previous submission which also subsumes the binary approach. For completeness and clarity of exposition, we develop the idea of well-composedness within the binary image context and later generalize the discussion to include those images with multiple labels.

Well-Composedness and the Topological Repairing of 2-D and 3-D Digital Images

Certain classes of images find disparate use amongst members of the ITK community for such purposes as visualization, simulation, testing, etc. Currently there exists two derived classes from the ImageSource class used for generating specific images for various applications, viz. RandomImageSource and GaussianImageSource. We propose to add to this set with the class GridImageSource which, obviously enough, produces a grid image. Such images are useful for visualizing deformation when used in conjunction with the WarpImageFilter, simulating magnetic resonance tagging images, or creating optical illusions with which to amaze your friends.

Gridding Graphic Graticules

Logic operations such as $I > J$, where I and J are images are useful
when generating masks. This submission includes $==$, $>$, $>=$, $<$
and $<=$ filters based on the existing logic filters. This is a very
minor submission - there may already be easy ways of performing the
logic operations defined by these filters, but I couldn't find them.

Some extra image logical operations

Homogeneous transformations are widely used in computer vision and graphics. They commonly arise
when considering the transformation of an image of a planar object under arbitrary camera motion, or the
transformation of two images of the same scene due to camera motion holding the optical center fixed.
In this paper we describe the addition of a class of homogeneous transforms for the Insight Toolkit.