This documents is about the filter itk::ScalarQuadEdgeMeshToListAdaptor. It takes the input mesh and generates a list of measurement vectors according to the scalars of the mesh. The list can be fed into itk::Statistics::SampleToHistogramFilter [1, 2] to generate a histogram about the scalars on the input mesh.
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.

Mesh To List Adaptor

The deconvolution, also called deblurring, tries to revert the optical distortion introduced during the aquisition of the image. It is a family of image processing which can be classed in the larger family of image restoration.
Deconvolution is a very difficult problem, and many algorithms have been proposed to solve it, with different strenght and weakness which may depend on the context where they are used. As a consequence, it is desirable to have several algorithms available when trying to restore some images. The different algorithms are often built on a similar principle, making possible to share a large part of their API in their implementation. Also, the most generic operations related to deconvolution should be reusable in order to avoid code duplication and ease the implementation of new algorithms.

In this contribution, the infrastructure for the implementation of several deconvolution algorithms is proposed. Based on this infrastructure, twelve simple deconvolution algorithms of reference are also provided.


Deconvolution: infrastructure and reference algorithms

The development of segmentation algorithms for liver tumors in CT scans has found growing attention in recent years. The validation of these methods, however, is often treated as a subordinate task. In this article, we review existing approaches and present rst steps towards a new methodology that evaluates the quality of an algorithm in relation to the variability of manual delineations. We obtained three manual segmentations for 50 liver lesions and computed the results of a segmentation algorithm. We compared all four masks with each other and with different ground truth estimates and calculated scores according to the validation framework from the MICCAI challenge 2008. Our results show some cases where this more elaborate evaluation reflects the segmentation quality in a more adequate way than traditional approaches. The concepts can also be extended to other similar segmentation problems.

Validation of Liver Tumor Segmentation in CT Scans by Relating Manual and Algorithmic Performance - A Preliminary Study

ITK is one of the most powerful image segmentation and registration libraries available as an open source toolkit. Motivated by the recent popularization of the iPhone, iPod touch, and iPad, in this work, we describe the set of required steps for integrating the ITK framework into Apple’s iOS mobile operating system. Our focus in this paper is on the process of importing the C++ based ITK to the Objective-C based iOS, and creating a simple application that demonstrates the ITK libraries are integrated. This paper brings to the reader a user-manual on how to integrate the ITK libraries into iOS applications and code. We present here the series of steps we performed in order to import the ITK libraries as well as report the results of importing them under different versions of the iOS and under different architectures (the Simulator and Device architecture).

ITK on the iOS

This document describes the use and implementation of first and second order spatial derivatives of coordinate transformations in the Insight Toolkit url{www.itk.org}). Spatial derivatives are useful for many types of regularising or penalty terms frequently used in image registration. These derivatives are dubbed 'SpatialJacobian' and 'SpatialHessian' to distinguish with the derivative to the transformation parameters themselves, which is called `Jacobian' in the ITK.

In addition to the spatial derivatives, we derived and implemented the derivatives to the registration/transform parameters of these spatial derivatives, required for gradient descent type optimisation routines. These derivatives are implemented in a sparse manner, reducing the computation time for transformations which have local support. All of these derivatives are implemented for the most common ITK coordinate transformation, such as the rigid, affine and B-spline transformation. In addition we derive formulae and code for arbitrary compositions of transformations. The spatial derivatives were subsequently exploited by implementing the bending energy penalty term.

This paper is accompanied with the source code, input data, parameters and output data that the authors 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.


Itk::Transforms supporting spatial derivatives

In this paper, we analyze the relationship between parameters of brain tumors obtained through in vivo magnetic resonance imaging (MRI), in vivo magneticnresonance spectroscopy (MRS), and ex vivo immunohistochemistry (IHC). The goal of our project is to provide a quantitative definition of tumor cellularity based on the in vivo parameters. Biopsy samples obtained from previously untreated patients with a diagnosis of GBM are used to find the link between imaging parameters at the specific biopsy locations and IHC parameters from the corresponding tissue samples. A functional tree (FT) model of tumor cellularity is learned from the in vivo parameters and the remaining histological parameters. The tumor cellularity model is then tested on examples which contain only in vivo parameters, by first estimating the remaining IHC parameters by applying the Expectation Maximization (EM) algorithm, and then using the complete parameter vector for classification.

Modeling Tumor Cellularity in Newly Diagnosed GBMs using MR Imaging and Spectroscopy

This paper presents a technique for immunostaining biomarker detection in digital slides. We treat immunostaining detection as a color image analysis problem and build statistical color models using a large number of labeled positive and negative immunostaining pixels. We have implemented the statistical models in different color spaces and show that the opponent chromaticity signals effectively characterize the color distributions of the immunostaining biomarkers and that the luminance is an unreliable and distractive signal. We have applied the technique to the detection of positive P53 immunostaining in digital slides of oesophagitis and colorectal biopsies. We present experimental results and show that the technique can achieve a biomarker detection rate of over 98% with 5% false positives. 

Biomarker Detection in Whole Slide Imaging based on Statistical Color Models

This paper presents a method for nonlinear registration of images, where there exists no one-to-one correspondence in parts of the image. Such a situation occurs for instance in the case where an atlas of normal anatomy shall be matched to pathological data, such as tumors, resections or lesions. Our idea is to use local confidence weights and to model pathological regions with zero confidence. We integrate this concept into the efficient and publicly available diffeomorphic demons registration framework. Finally, we show that this process better captures deformations in high-confidence regions than without using the proposed modification. Furthermore, it is easy to implement and runs faster than previous approaches.

Atlas to Image-with-Tumor Registration Based on Demons and Deformation Inpainting

We have recently detailed the procedure for building ITK on the iOS. In this work, we contribute itkiOSImageIO , the necessary ITK class that provides the input/output (IO) interface with the repository of images stored on iOS Apple devices, such as the iPod touch, iPhone, or iPad. The proposed classes provide the ITK programmer with the facility to read or write iOS images. Along with contributing the source code for these classes, we provide examples of reading and writing different types of gray 
level and color iOS images, as well as ﬁltering the images using ITK ﬁlters. 

ITK Image IO interface with Apple iOS

ITK has a powerful and flexible image IO framework. Reading and writing different types of image file is straightforward. The image IO could be easily extended by writing a specific image IO class for a new image file format. Whereas, there is no such framework for easily reading and writing itk::Mesh/itk::QuadEdgeMesh, At the moment, only a few specific classes to read vtk polydata format and is not easily to be extended. This paper describes our contribution to itk for providing a mesh IO framework which could be used for reading and writing some commonly used mesh file formats. The mesh IO classes worked well for both itk::Mesh and itk::QuadEdgeMesh.