Hip2Norm: an object-oriented cross-platform program for 3D analysis of hip joint morphology using 2D pelvic radiographs

We developed an object-oriented cross-platform program to perform three-dimensional (3D) analysis of hip joint morphology using two-dimensional (2D) anteroposterior (AP) pelvic radiographs. Landmarks extracted from 2D AP pelvic radiographs and optionally an additional lateral pelvic X-ray were combined with a cone beam projection model to reconstruct 3D hip joints. Since individual pelvic orientation can vary considerably, a method for standardizing pelvic orientation was implemented to determine the absolute tilt/rotation. The evaluation of anatomically morphologic differences was achieved by reconstructing the projected acetabular rim and the measured hip parameters as if obtained in a standardized neutral orientation. The program had been successfully used to interactively objectify acetabular version in hips with femoro-acetabular impingement or developmental dysplasia. Hip(2)Norm is written in object-oriented programming language C++ using cross-platform software Qt (TrollTech, Oslo, Norway) for graphical user interface (GUI) and is transportable to any platform.

Context Aware e-Learning Objects and their Types from the Perspective of the Object-Oriented Paradigm

The contribution of this article demonstrates how to identify context-aware types of e-Learning objects (eLOs) derived from the subject domains. This perspective is taken from an engineering point of view and is applied during requirements elicitation and analysis relating to present work in constructing an object-oriented (OO), dynamic, and adaptive model to build and deliver packaged e-Learning courses. Consequently, three preliminary subject domains are presented and, as a result, three primitive types of eLOs are posited. These types educed from the subject domains are of structural, conceptual, and granular nature. Structural objects are responsible for the course itself, conceptual objects incorporate adaptive and logical interoperability, while granular objects congregate granular assets. Their differences, interrelationships, and responsibilities are discussed. A major design challenge relates to adaptive behaviour. Future research addresses refinement on the subject domains and adaptive hypermedia systems.

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Object-Oriented Legacy System Trace-based Logic Testing

When reengineering legacy systems, it is crucial to assess if the legacy behavior has been preserved or how it changed due to the reengineering effort. Ideally if a legacy system is covered by tests, running the tests on the new version can identify potential differences or discrepancies. However, writing tests for an unknown and large system is difficult due to the lack of internal knowledge. It is especially difficult to bring the system to an appropriate state. Our solution is based on the acknowledgment that one of the few trustable piece of information available when approaching a legacy system is the running system itself. Our approach reifies the execution traces and uses logic programming to express tests on them. Thereby it eliminates the need to programatically bring the system in a particular state, and handles the test-writer a high-level abstraction mechanism to query the trace. The resulting system, called TESTLOG, was used on several real-world case studies to validate our claims.

Practical Object-Oriented Back-in-Time Debugging

Back-in-time debuggers are extremely useful tools for identifying the causes of bugs, as they allow us to inspect the past states of objects no longer present in the current execution stack. Unfortunately the "omniscient" approaches that try to remember all previous states are impractical because they either consume too much space or they are far too slow. Several approaches rely on heuristics to limit these penalties, but they ultimately end up throwing out too much relevant information. In this paper we propose a practical approach to back-in-time debugging that attempts to keep track of only the relevant past data. In contrast to other approaches, we keep object history information together with the regular objects in the application memory. Although seemingly counter-intuitive, this approach has the effect that past data that is not reachable from current application objects (and hence, no longer relevant) is automatically garbage collected. In this paper we describe the technical details of our approach, and we present benchmarks that demonstrate that memory consumption stays within practical bounds. Furthermore since our approach works at the virtual machine level, the performance penalty is significantly better than with other approaches.

Object-Oriented Reengineering Patterns

The rapid growth of object-oriented development over the past twenty years has given rise to many object-oriented systems that are large, complex and hard to maintain. Object-Oriented Reengineering Patterns addresses the problem of understanding and reengineering such object-oriented legacy systems. This book collects and distills successful techniques in planning a reengineering project, reverse-engineering, problem detection, migration strategies and software redesign. The material in this book is presented as a set of "reengineering patterns" --- recurring solutions that experts apply while reengineering and maintaining object-oriented systems. The principles and techniques described in this book have been observed and validated in a number of industrial projects, and reflect best practice in object-oriented reengineering.

A new approach to detect structural differences in lung parenchyma using digital image analysis

Morphometric investigations using a point and intersection counting strategy in the lung often are not able to reveal the full set of morphologic changes. This happens particularly when structural modifications are not expressed in terms of volume density changes and when rough and fine surface density alterations cancel each other at different magnifications. Making use of digital image processing, we present a methodological approach that allows to easily and quickly quantify changes of the geometrical properties of the parenchymal lung structure and reflects closely the visual appreciation of the changes. Randomly sampled digital images from light microscopic sections of lung parenchyma are filtered, binarized, and skeletonized. The lung septa are thus represented as a single-pixel wide line network with nodal points and end points and the corresponding internodal and end segments. By automatically counting the number of points and measuring the lengths of the skeletal segments, the lung architecture can be characterized and very subtle structural changes can be detected. This new methodological approach to lung structure analysis is highly sensitive to morphological changes in the parenchyma: it detected highly significant quantitative alterations in the structure of lungs of rats treated with a glucocorticoid hormone, where the classical morphometry had partly failed.

Glucocorticoid induced impairment of lung structure assessed by digital image analysis

Glucocorticoids (GC) are successfully applied in neonatology to improve lung maturation in preterm born babies. Animal studies show that GC can also impair lung development. In this investigation, we used a new approach based on digital image analysis. Microscopic images of lung parenchyma were skeletonised and the geometrical properties of the septal network characterised by analysing the 'skeletal' parameters. Inhibition of the process of alveolarisation after extensive administration of small doses of GC in newborn rats was confirmed by significant changes in the 'skeletal' parameters. The induced structural changes in the lung parenchyma were still present after 60 days in adult rats, clearly indicating a long lasting or even definitive impairment of lung development and maturation caused by GC. Conclusion: digital image analysis and skeletonisation proved to be a highly suited approach to assess structural changes in lung parenchyma.

Mapping material distribution in a heterogeneous sand tank by image analysis

Water flow and solute transport through soils are strongly influenced by the spatial arrangement of soil materials with different hydraulic and chemical properties. Knowing the specific or statistical arrangement of these materials is considered as a key toward improved predictions of solute transport. Our aim was to obtain two-dimensional material maps from photographs of exposed profiles. We developed a segmentation and classification procedure and applied it to the images of a very heterogeneous sand tank, which was used for a series of flow and transport experiments. The segmentation was based on thresholds of soil color, estimated from local median gray values, and of soil texture, estimated from local coefficients of variation of gray values. Important steps were the correction of inhomogeneous illumination and reflection, and the incorporation of prior knowledge in filters used to extract the image features and to smooth the results morphologically. We could check and confirm the success of our mapping by comparing the estimated with the designed sand distribution in the tank. The resulting material map was used later as input to model flow and transport through the sand tank. Similar segmentation procedures may be applied to any high-density raster data, including photographs or spectral scans of field profiles.

Image analysis of immunohistochemistry is superior to visual scoring as shown for patient outcome of esophageal adenocarcinoma.

Quantification of protein expression based on immunohistochemistry (IHC) is an important step in clinical diagnoses and translational tissue-based research. Manual scoring systems are used in order to evaluate protein expression based on staining intensities and distribution patterns. However, visual scoring remains an inherently subjective approach. The aim of our study was to explore whether digital image analysis proves to be an alternative or even superior tool to quantify expression of membrane-bound proteins. We analyzed five membrane-binding biomarkers (HER2, EGFR, pEGFR, β-catenin, and E-cadherin) and performed IHC on tumor tissue microarrays from 153 esophageal adenocarcinomas patients from a single center study. The tissue cores were scored visually applying an established routine scoring system as well as by using digital image analysis obtaining a continuous spectrum of average staining intensity. Subsequently, we compared both assessments by survival analysis as an end point. There were no significant correlations with patient survival using visual scoring of β-catenin, E-cadherin, pEGFR, or HER2. In contrast, the results for digital image analysis approach indicated that there were significant associations with disease-free survival for β-catenin, E-cadherin, pEGFR, and HER2 (P = 0.0125, P = 0.0014, P = 0.0299, and P = 0.0096, respectively). For EGFR, there was a greater association with patient survival when digital image analysis was used compared to when visual scoring was (visual: P = 0.0045, image analysis: P < 0.0001). The results of this study indicated that digital image analysis was superior to visual scoring. Digital image analysis is more sensitive and, therefore, better able to detect biological differences within the tissues with greater accuracy. This increased sensitivity improves the quality of quantification.