This document describes an implementation of an alternative bilateral filtering algorithm in ITK.
This class implements the fast bilateral filter algorithm in ITK by taking the input image and organizing it into the required data structure as an ITK Image with dimension one greater than the original image. The DiscreteGaussianImageFilter is then used to blur the higher dimensional image. The discrete Gaussian image filter was chosen because the kernel width used should usually be small due to the down-sampling involved when using this technique. As described in the ITK documentation, this class should outperform the recursive Gaussian algorithm due to the small kernel size DiscreteGaussianImageFilter.After the Gaussian blur has been completed the data will be interpolated and written to the output image.

By incorporating this class into the ITK framework, this more efficient algorithm will be made available to all ITK users.

A Fast Approximation to the Bilateral Filter for ITK

This document presents a set of classes to read a PTX file (a common file format produced by LiDAR scanners) into an itkMesh object. We propose these classes as addition to the Insight Toolkit ITK (www.itk.org).

Reading a PTX file into an itkMesh

Gross shape measures such as volume have been widely used in statistical analysis of anatomical structures. Statistical shape analysis methods have emerged within the last decade to allow for a localized analysis of shape. Most shape analysis frameworks are though lacking a good statistical underpinning, as they commonly do not allow for the inclusion of independent variables such as age, gender or clinical
scores. This work presents a unified method for local shape analysis that can accomodate different number of variates and contrasts. It also allows to include any number of associated variables in the statistical analysis of the data. Several cases of study are given to clarify the explanation of the different types of data that can be analyzed and the parameters that can be used to tune the program shapeAnalysisMANCOVA. This tool has been designed to interact seamlessly with the existing UNC SPHARM-PDM based shape analysis toolbox.

Local Shape Analysis using MANCOVA

In recent years, Light Detection and Ranging (LiDAR) scanners have become more prevalent in the scientific community. They capture a "2.5-D" image of a scene by sending out thousands of laser pulses and using time-of-flight calculations to determine the distance to the first reflecting surface in the scene. Rather than setting up a collection of objects in real life and actually sending lasers into the scene, one can simply create a scene out of 3d models and "scan" it by casting rays at the models. This is a great resource for any researchers who work with 3D model/surface/point data and LiDAR data. The synthetic scanner can be used to produce data sets for which a ground truth is known in order to ensure algorithms are behaving properly before moving to "real" LiDAR scans. Also, noise can be added to the points to attempt to simulate a real LiDAR scan for researchers who do not have access to the very expensive equipment required to obtain real scans.

A Synthetic LiDAR Scanner for VTK

Many neuroanatomy studies rely on brain tissue segmentation in Magnetic Resonance images (MRI). The Expectation-Maximization (EM) theory offers a popular framework for this task. We studied the EM algorithm developed at the Surgical Planning Laboratory (SPL) at Harvard Medical School and implemented in the Slicer3 software. We observed that the segmentation lacks accuracy if the image exhibits some intensity inhomogeneity. Moreover the optimum parameters are challenging to estimate. This document aims at describing our solutions within the context of statistical modeling. Our contributions range from algorithm improvements to novel representations of the statistical distribution model. First we added a bias field correction module and exposed the most significant parameters. Second we proposed a new way to select the distribution of the tissues to be segmented. Finally we designed a set of interactive tools to make the segmentation process easier and more accurate. To validate the new segmentation pipeline, we performed our experiments on MRI data and a clinical expert evaluated our results.

The source code developed for this work, i.e. the code of the MRIBiasFieldCorrection and EMSegment modules, is part of Slicer3 version 3.5 and can be downloaded by this command: svn co http://svn.slicer.org/Slicer3/trunk Slicer3

New Expectation Maximization Segmentation Pipeline in Slicer 3

This paper describes the Carotid Lumen Segmentation and Stenosis Grading Challenge, which was part of the workshop ``3D Segmentation in the Clinic: A Grand Challenge''. This workshop was part of the 12th conference on Medical Image Computing and Computer Assisted Interventions, held in London in September 2009. We introduce the purpose and background of the challenge and describe the data, reference standard and evaluation measures that were used in this challenge. We also provide the results of the data that were submitted by industrial and academic research institutes.

Carotid Lumen Segmentation and Stenosis Grading Challenge

An Insight Toolkit (ITK) algorithm for detection of spherical objects using Hough methods with voting is presented in this paper. Currently, the usage of Hough methods for detecting linear and circular elements exists for 2D images in ITK. The current work extends those filters in several ways. Firstly, the new filters operate on N-dimensional images. Secondly, they work in physical coordinates which is quite essential in medical imaging modalities. Thirdly, they are optimized (multi-threaded execution, stratified sampling etc.) for usage on large datasets and show a significant speedup even in 2D and on small images. Our implementation follows the same underlying mathematics of Hough transforms (as implemented by the 2D filters) but with some minor variations. The main variation lies in the pattern of voting that involves selecting voting regions easily and efficiently accessible to region iterators rather than cones that are difficult to generalize in higher dimensions. We include 2D example code, parameter settings and show the results generated on embryonic images of the zebrafish from optical microscopy.

An Optimized N-Dimensional Hough Filter for Detecting Spherical Image Objects

In this paper, we present a semi-automtic method for segmenting carotid arteries in contrast enhanced (CE)-CT angiography (CTA) scans. 
The segmentation algorithm extracts the lumen of carotid arteries between user specfied locations. Specifically, the algorithm first 
detects the centerline representations between the user placed seed points. This centerline extraction algorithm is based on a minimal path 
detection algorithm which operates on a {\em medialness} map. The lumen of corotid arteries is extracted by using 
the global optimal graph-cuts algorithm~\cite{boykov:01} using the centerlines as input. The radius information contained 
in the centerline representation
is used to normalize the gradient based weights of the graph. It is shown that this algorithm can sucessfully segment the carotid arteries without
including calcified and non-calcified plaques in the segmentation results.

Segmentation of Carotid Arteries By Graph-Cuts Using Centerline Models

We describe a new class itk::DicomDirectoryToImage which, given a directory consisting of raw dicom files, converts all found series to volumetric data with consistent filenaming based on the dicom headers. In addition various internal checks on the dicom files are made and reported, and the user has the possibility to add simple criteria to match with the series description. The main advantage of the class is the ease of processing and converting very large dicom databases in a consistent way.

DicomImport

This paper presents parsing software for importing contours from DICOM RT structure set files into binary image files. This is a common requirement for transferring expert manual segmentations from radiotherapy treatment planning software into external image analysis environments (such as ITK). We provide an implementation of RT structure set importing and give some example results from prostate cancer radiotherapy planning volumes. 

Please note that this implementation requires GDCM 2.0 to be installed (this is trivial; however this requirement is the reason for the low automatic testing score by the Insight-Journal Dashboard).

Please Note: Updated June 2010 for a couple of minor issues (pls. see comments in .cxx) and to remove the requirement for image template. This code now reads the dcm directory and creates a 3D volume and associated binary structure volumes (all in NIfTI format).
Usage: itkDICOMRT DicomDirectory inputDICOMRTImageFile OutputFilePrefix

Feb 2013: Please note that this code won't work with ITK4 and above - instead please see: http://hdl.handle.net/10380/3401 . There is also a new test DICOM-RT directory with that paper. 

Mar 2013 Now correctly handles holes within structures. Thanks to Sebastian Steger for the code fix.
